Qmcchem - User contributions [en]

File:Helix hires.png

2017-09-19T13:35:18Z

Scemama:

File:Strand hires.png

2017-09-19T13:34:59Z

Scemama:

Communication

2017-09-19T13:34:29Z

Scemama:

== Video files ==

=== Full beta-amyloid QMC simulation ===

Download video files here:
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mpg .mpg format]
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mp4 .mp4 format]

=== Equip@Meso presentation ===

http://vod-flash.u-strasbg.fr:8080/canalc2/2012/1017_equipe_at_meso/20121018_11_scemama_equip@meso.mp4

=== TGCC/Curie on France 3 ===

http://www-hpc.cea.fr/fr/complexe/FRANCE_3_reportage_Curie_CEA_130312.swf

=== N2 animation with local energy graph ===

* [http://qmcchem.ups-tlse.fr/files/scemama/n2.gif Animated gif]
* [http://qmcchem.ups-tlse.fr/files/scemama/n2.mpeg MPEG]

== Pictures ==

=== QMC ===

[[File:Qmc.png|600px]]

[[File:qmc_hydrogen.png|600px]]

Overview of a Quantum Monte Carlo simulation. Apart from the initialization and finalization step, all the processes are completely independent.

=== Beta-amyloid ===

[[File:peptide.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Different electron colors represent different time steps.

[[File:peptide_nozoom.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Electrons are repesented in gold, and a few
time steps in the past are represented as the footprints of the electrons.

[[File:alpha-beta.png|600px]]

The two beta-amyloid structures simulated on Curie in Dec 2011 (122 atoms, 434 electrons). The energy difference between these two structures was computed with QMC=Chem.

[[File:peptide_cu.png|600px]]

[[File:alzheimer.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid fragment with a Copper atom.

High resolution pictures:

[[File:strand_hires.png|600px]]
[[File:helix_hires.png|600px]]

Communication

2016-11-22T09:43:11Z

Scemama: /* Full beta-amyloid QMC simulation */

Current scientific activities

2016-11-22T09:42:17Z

Scemama:

* [[Calculation of forces]]
* [[Multi-Jastrow wave functions]]
* [[Realistic chemical systems]]
* [[QMCChem|The QMC=Chem code]]

Quantum Monte Carlo for Chemistry @ Toulouse

2015-07-13T13:45:49Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* June 2015: [http://www8.hp.com/h20195/v2/GetPDF.aspx/4AA5-9253ENW.pdf Université Paul Sabatier saves space and energy with HP Moonshot]

* December 2014: QMC=Chem was selected for the France-Grilles Cloud Challenge. [https://webcast.in2p3.fr/videos-retour_dexperience_sur_lutilisation_de_services_francegrilles_projet_challenge_fg Video]

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]. 12000 cores ran for 48 hours.

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 TFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2015-07-13T13:45:34Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* June 2015: [http://www8.hp.com/h20195/v2/GetPDF.aspx/4AA5-9253ENW.pdf Université Paul Sabatier saves space and energy with HP Moonshot]

* December 2014: QMC=Chem was selected for the France-Grilles Cloud Challenge.
[https://webcast.in2p3.fr/videos-retour_dexperience_sur_lutilisation_de_services_francegrilles_projet_challenge_fg Video]

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]. 12000 cores ran for 48 hours.

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 TFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2015-07-13T13:43:47Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* June 2015: [http://www8.hp.com/h20195/v2/GetPDF.aspx/4AA5-9253ENW.pdf Université Paul Sabatier saves space and energy with HP Moonshot]

* December 2014: QMC=Chem was selected for the France-Grilles Cloud Challenge.

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]. 12000 cores ran for 48 hours.

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 TFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2015-06-27T20:03:49Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* December 2014: QMC=Chem was selected for the France-Grilles Cloud Challenge.

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]. 12000 cores ran for 48 hours.

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 TFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2015-06-05T22:50:44Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* December 2015: QMC=Chem was selected for the France-Grilles Cloud Challenge.

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]. 12000 cores ran for 48 hours.

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 TFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2015-01-25T15:26:24Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* December 2015: QMC=Chem was selected for the France-Grilles Cloud Challenge.

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]. 12000 cores ran for 48 hours.

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 TFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2015-01-25T15:26:04Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* December 2015: QMC=Chem was selected for the France-Grilles Cloud Challenge.

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS].
12000 cores ran for 48 hours.

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 TFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

QMCChem

2014-11-21T11:03:35Z

Scemama: /* Current Features */

During the last years we have been actively developing the QMC=Chem quantum Monte Carlo code. This code was initially designed for massively parallel simulations, and uses the manager/worker model.

[[File:manager_worker.png|center]]

When the program starts its execution, the manager runs on the master node and spawns two other processes: a worker process and a data server. The worker is an efficient Fortran executable with minimal memory and disk space requirements (typically a few megabytes for each), where the only MPI communication is the broadcast of the input data (wave function parameters, initial positions in the 3N-space and random seed). The outline of the task of a worker is the following:

while ( Running )
{
compute_a_block_of_data();
Running = send_the_results_to_the_data_server();
}

The data server is a socket server implemented in Python. When it receives the computed data of a worker, it replies to the worker the order given by the manager to compute another block or to stop. The received data is then stored in a database using an asynchronous I/O mechanism. The manager is always aware of the results computed by all the workers and controls the running/stopping state of the workers and the interaction of the user during the simulation.

QMC=Chem is very well suited to massive parallelism and cloud computing:
* All the implemented algorithms are CPU-bound
* All workers are totally independent
* The load balancing is optimal: the workers always work 100% of the time, independently of their respective CPU speeds
* The code was written to be as portable as possible: the manager is written in standard Python and the worker is written in standard Fortran.
* The network traffic is minimal and the amount of data transferred over the network can even be adjusted by the user
* The number of simultaneous worker nodes can be variable during a calculation
* Fault-tolerance is implemented
* The input and output data are not presented as traditional input files and output files. All the input and output data are stored in a database and an API is provided to access the data. This allows different forms of interaction of the user: scripts, graphical user interfaces, command-line tools, web interfaces, etc.

== Current Features ==

[[File:screenshot.png|center|500px]]

=== Methods ===
* VMC
* DMC with stochastic reconfiguration
* Jastrow factor optimization
* CI coefficients optimization

=== Wave functions ===
* Single determinant
* Multi-determinant
* Nuclear cusp correction

=== Links ===
* Easy development with the [http://irpf90.ups-tlse.fr/ IRPF90 tool].
* The access to input/output files is provided via an API produced by the [http://ezfio.sf.net Easy Fortran I/O library generator]
* [{{SERVER}}/qmcchem_doc/QMC_Chem_Documentation.html Documentation]

== Parallel speed-up curve ==

[[File:qmcchem_speedup.png|400px|left]]
Number of computed blocks for the CuCl2 molecule. The simulation stops when the wall-time has reached 5 minutes. Each block is composed of 10 walkers realizing 2,000 VMC steps.

 

== Features under development ==

=== Properties ===
* Molecular Forces
* Moments (dipole, quadrupole,...)
* Electron density

=== Practical aspects ===
* Web interface for input and output

== Input file creation ==

The QMC=Chem input file can be created using the [{{SERVER}}/qmcchem_input.py web interface]. Upload a Q5Cost file or an output file from GAMESS, Gaussian or Molpro, and you will download the QMC=Chem input directory.

== Papers related to the QMC=Chem code ==

* [http://dx.doi.org/10.1002/jcc.23216 Quantum Monte Carlo for large chemical systems: Implementing efficient strategies for petascale platforms and beyond]
* [http://link.springer.com/chapter/10.1007/978-1-4614-0508-5_13 QMC=Chem: a quantum Monte Carlo program for large-scale simulations in chemistry at the petascale level and beyond]
* [http://link.springer.com/chapter/10.1007/978-1-4614-0508-5_13 Large-scale quantum Monte Carlo electronic structure calculations on the EGEE grid]

File:Screenshot.png

2014-11-21T11:02:24Z

Scemama:

QMCChem

2014-11-21T11:02:08Z

Scemama:

During the last years we have been actively developing the QMC=Chem quantum Monte Carlo code. This code was initially designed for massively parallel simulations, and uses the manager/worker model.

[[File:manager_worker.png|center]]

When the program starts its execution, the manager runs on the master node and spawns two other processes: a worker process and a data server. The worker is an efficient Fortran executable with minimal memory and disk space requirements (typically a few megabytes for each), where the only MPI communication is the broadcast of the input data (wave function parameters, initial positions in the 3N-space and random seed). The outline of the task of a worker is the following:

while ( Running )
{
compute_a_block_of_data();
Running = send_the_results_to_the_data_server();
}

The data server is a socket server implemented in Python. When it receives the computed data of a worker, it replies to the worker the order given by the manager to compute another block or to stop. The received data is then stored in a database using an asynchronous I/O mechanism. The manager is always aware of the results computed by all the workers and controls the running/stopping state of the workers and the interaction of the user during the simulation.

QMC=Chem is very well suited to massive parallelism and cloud computing:
* All the implemented algorithms are CPU-bound
* All workers are totally independent
* The load balancing is optimal: the workers always work 100% of the time, independently of their respective CPU speeds
* The code was written to be as portable as possible: the manager is written in standard Python and the worker is written in standard Fortran.
* The network traffic is minimal and the amount of data transferred over the network can even be adjusted by the user
* The number of simultaneous worker nodes can be variable during a calculation
* Fault-tolerance is implemented
* The input and output data are not presented as traditional input files and output files. All the input and output data are stored in a database and an API is provided to access the data. This allows different forms of interaction of the user: scripts, graphical user interfaces, command-line tools, web interfaces, etc.

== Current Features ==

[[File:screenshot.png|center]]

=== Methods ===
* VMC
* DMC with stochastic reconfiguration
* Jastrow factor optimization
* CI coefficients optimization

=== Wave functions ===
* Single determinant
* Multi-determinant
* Nuclear cusp correction

=== Links ===
* Easy development with the [http://irpf90.ups-tlse.fr/ IRPF90 tool].
* The access to input/output files is provided via an API produced by the [http://ezfio.sf.net Easy Fortran I/O library generator]
* [{{SERVER}}/qmcchem_doc/QMC_Chem_Documentation.html Documentation]

== Parallel speed-up curve ==

[[File:qmcchem_speedup.png|400px|left]]
Number of computed blocks for the CuCl2 molecule. The simulation stops when the wall-time has reached 5 minutes. Each block is composed of 10 walkers realizing 2,000 VMC steps.

 

== Features under development ==

=== Properties ===
* Molecular Forces
* Moments (dipole, quadrupole,...)
* Electron density

=== Practical aspects ===
* Web interface for input and output

== Input file creation ==

The QMC=Chem input file can be created using the [{{SERVER}}/qmcchem_input.py web interface]. Upload a Q5Cost file or an output file from GAMESS, Gaussian or Molpro, and you will download the QMC=Chem input directory.

== Papers related to the QMC=Chem code ==

* [http://dx.doi.org/10.1002/jcc.23216 Quantum Monte Carlo for large chemical systems: Implementing efficient strategies for petascale platforms and beyond]
* [http://link.springer.com/chapter/10.1007/978-1-4614-0508-5_13 QMC=Chem: a quantum Monte Carlo program for large-scale simulations in chemistry at the petascale level and beyond]
* [http://link.springer.com/chapter/10.1007/978-1-4614-0508-5_13 Large-scale quantum Monte Carlo electronic structure calculations on the EGEE grid]

Quantum Monte Carlo for Chemistry @ Toulouse

2014-07-11T09:32:29Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 TFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2014-06-23T15:51:22Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [http://www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2014-06-23T15:50:59Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* June 2014: QMC=Chem was selected for a meso-challenge on the new CALMIP system [www.calmip.univ-toulouse.fr/spip/spip.php?article388 EOS]

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

File:Alzheimer.png

2013-07-26T15:47:47Z

Scemama: uploaded a new version of "File:Alzheimer.png"

File:Alzheimer.png

2013-07-26T15:46:50Z

Scemama:

Communication

2013-07-26T15:46:27Z

Scemama: /* Beta-amyloid */

== Video files ==

=== Full beta-amyloid QMC simulation ===

Download video files here:
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mpg .mpg format]
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mp4 .mp4 format]

or view on-line:

<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

=== Equip@Meso presentation ===

http://vod-flash.u-strasbg.fr:8080/canalc2/2012/1017_equipe_at_meso/20121018_11_scemama_equip@meso.mp4

=== TGCC/Curie on France 3 ===

http://www-hpc.cea.fr/fr/complexe/FRANCE_3_reportage_Curie_CEA_130312.swf

=== N2 animation with local energy graph ===

* [http://qmcchem.ups-tlse.fr/files/scemama/n2.gif Animated gif]
* [http://qmcchem.ups-tlse.fr/files/scemama/n2.mpeg MPEG]

== Pictures ==

=== QMC ===

[[File:Qmc.png|600px]]

[[File:qmc_hydrogen.png|600px]]

Overview of a Quantum Monte Carlo simulation. Apart from the initialization and finalization step, all the processes are completely independent.

=== Beta-amyloid ===

[[File:peptide.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Different electron colors represent different time steps.

[[File:peptide_nozoom.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Electrons are repesented in gold, and a few
time steps in the past are represented as the footprints of the electrons.

[[File:alpha-beta.png|600px]]

The two beta-amyloid structures simulated on Curie in Dec 2011 (122 atoms, 434 electrons). The energy difference between these two structures was computed with QMC=Chem.

[[File:peptide_cu.png|600px]]

[[File:alzheimer.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid fragment with a Copper atom.

Quantum Monte Carlo for Chemistry @ Toulouse

2013-07-24T16:09:09Z

Scemama: /* QMC in a few words */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2013-07-24T16:08:09Z

Scemama: /* QMC in a few words */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==

Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

 
[[File:Qmc.png|400px|center]]
[[File:qmc_hydrogen.png|1000px|center]]

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2013-07-24T16:05:05Z

Scemama: /* QMC in a few words */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

[[File:qmc_hydrogen.png|1000px]]

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2013-07-24T16:04:07Z

Scemama: /* QMC in a few words */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

[[File:QMC_hydrogen.png|1000px]]

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

File:Qmc hydrogen.png

2013-07-24T16:01:48Z

Scemama: uploaded a new version of "File:Qmc hydrogen.png"

File:Qmc hydrogen.png

2013-07-24T15:54:12Z

Scemama:

Communication

2013-07-24T15:52:01Z

Scemama:

== Video files ==

=== Full beta-amyloid QMC simulation ===

Download video files here:
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mpg .mpg format]
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mp4 .mp4 format]

or view on-line:

<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

=== Equip@Meso presentation ===

http://vod-flash.u-strasbg.fr:8080/canalc2/2012/1017_equipe_at_meso/20121018_11_scemama_equip@meso.mp4

=== TGCC/Curie on France 3 ===

http://www-hpc.cea.fr/fr/complexe/FRANCE_3_reportage_Curie_CEA_130312.swf

=== N2 animation with local energy graph ===

* [http://qmcchem.ups-tlse.fr/files/scemama/n2.gif Animated gif]
* [http://qmcchem.ups-tlse.fr/files/scemama/n2.mpeg MPEG]

== Pictures ==

=== QMC ===

[[File:Qmc.png|600px]]

[[File:qmc_hydrogen.png|600px]]

Overview of a Quantum Monte Carlo simulation. Apart from the initialization and finalization step, all the processes are completely independent.

=== Beta-amyloid ===

[[File:peptide.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Different electron colors represent different time steps.

[[File:peptide_nozoom.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Electrons are repesented in gold, and a few
time steps in the past are represented as the footprints of the electrons.

[[File:alpha-beta.png|600px]]

The two beta-amyloid structures simulated on Curie in Dec 2011 (122 atoms, 434 electrons). The energy difference between these two structures was computed with QMC=Chem.

[[File:peptide_cu.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid fragment with a Copper atom.

Quantum Monte Carlo for Chemistry @ Toulouse

2013-07-17T10:08:48Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris. [{{SERVER}}/files/scemama/Tutorial_QMC=Chem.pdf Slides available here]

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Communication

2013-07-17T07:33:39Z

Scemama: /* Full beta-amyloid QMC simulation */

== Video files ==

=== Full beta-amyloid QMC simulation ===

Download video files here:
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mpg .mpg format]
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mp4 .mp4 format]

or view on-line:

<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

=== Equip@Meso presentation ===

http://vod-flash.u-strasbg.fr:8080/canalc2/2012/1017_equipe_at_meso/20121018_11_scemama_equip@meso.mp4

=== TGCC/Curie on France 3 ===

http://www-hpc.cea.fr/fr/complexe/FRANCE_3_reportage_Curie_CEA_130312.swf

=== N2 animation with local energy graph ===

* [http://qmcchem.ups-tlse.fr/files/scemama/n2.gif Animated gif]
* [http://qmcchem.ups-tlse.fr/files/scemama/n2.mpeg MPEG]

== Pictures ==

=== QMC ===

[[File:Qmc.png|600px]]

Overview of a Quantum Monte Carlo simulation. Apart from the initialization and finalization step, all the processes are completely independent.

=== Beta-amyloid ===

[[File:peptide.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Different electron colors represent different time steps.

[[File:peptide_nozoom.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Electrons are repesented in gold, and a few
time steps in the past are represented as the footprints of the electrons.

[[File:alpha-beta.png|600px]]

The two beta-amyloid structures simulated on Curie in Dec 2011 (122 atoms, 434 electrons). The energy difference between these two structures was computed with QMC=Chem.

[[File:peptide_cu.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid fragment with a Copper atom.

Quantum Monte Carlo for Chemistry @ Toulouse

2013-07-17T07:28:10Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2013: QMC=Chem tutorial, Cecam workshop [http://www.cecam.org/workshop-942.html Atomistic and molecular simulations on massively parallel architectures], Paris.

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

People involved

2013-05-06T12:35:32Z

Scemama:

;[mailto:caffarel@irsamc.ups-tlse.fr Michel Caffarel]
:Directeur de Recherches, CNRS
:[[Michel Caffarel | Web Page]]

;[mailto:scemama@irsamc.ups-tlse.fr Anthony Scemama]
:Ingénieur de Recherche, CNRS
:[http://scemama.mooo.com Web page]

;[mailto:t.applencourt@gmail.com Thomas Applencourt]
:Doctorant

;[mailto:emmanuel.giner@irsamc.ups-tlse.fr Emmanuel Giner]
:Doctorant

File:Peptide cu.png

2013-05-06T12:33:56Z

Scemama: uploaded a new version of "File:Peptide cu.png"

File:Alpha-beta.png

2013-05-06T12:32:43Z

Scemama: uploaded a new version of "File:Alpha-beta.png"

File:Peptide nozoom.png

2013-05-06T12:31:44Z

Scemama: uploaded a new version of "File:Peptide nozoom.png"

File:Anr.gif

2013-05-06T12:30:19Z

Scemama: uploaded a new version of "File:Anr.gif"

QMCChem

2013-01-30T12:55:31Z

Scemama:

During the last years we have been actively developing the QMC=Chem quantum Monte Carlo code. This code was initially designed for massively parallel simulations, and uses the manager/worker model.

[[File:manager_worker.png|center]]

When the program starts its execution, the manager runs on the master node and spawns two other processes: a worker process and a data server. The worker is an efficient Fortran executable with minimal memory and disk space requirements (typically a few megabytes for each), where the only MPI communication is the broadcast of the input data (wave function parameters, initial positions in the 3N-space and random seed). The outline of the task of a worker is the following:

while ( Running )
{
compute_a_block_of_data();
Running = send_the_results_to_the_data_server();
}

The data server is a socket server implemented in Python. When it receives the computed data of a worker, it replies to the worker the order given by the manager to compute another block or to stop. The received data is then stored in a database using an asynchronous I/O mechanism. The manager is always aware of the results computed by all the workers and controls the running/stopping state of the workers and the interaction of the user during the simulation.

QMC=Chem is very well suited to massive parallelism and cloud computing:
* All the implemented algorithms are CPU-bound
* All workers are totally independent
* The load balancing is optimal: the workers always work 100% of the time, independently of their respective CPU speeds
* The code was written to be as portable as possible: the manager is written in standard Python and the worker is written in standard Fortran.
* The network traffic is minimal and the amount of data transferred over the network can even be adjusted by the user
* The number of simultaneous worker nodes can be variable during a calculation
* Fault-tolerance is implemented
* The input and output data are not presented as traditional input files and output files. All the input and output data are stored in a database and an API is provided to access the data. This allows different forms of interaction of the user: scripts, graphical user interfaces, command-line tools, web interfaces, etc.

== Current Features ==

=== Methods ===
* VMC
* DMC with stochastic reconfiguration
* Jastrow factor optimization
* CI coefficients optimization

=== Wave functions ===
* Single determinant
* Multi-determinant
* Nuclear cusp correction

=== Links ===
* Easy development with the [http://irpf90.ups-tlse.fr/ IRPF90 tool].
* The access to input/output files is provided via an API produced by the [http://ezfio.sf.net Easy Fortran I/O library generator]
* [{{SERVER}}/qmcchem_doc/QMC_Chem_Documentation.html Documentation]

== Parallel speed-up curve ==

[[File:qmcchem_speedup.png|400px|left]]
Number of computed blocks for the CuCl2 molecule. The simulation stops when the wall-time has reached 5 minutes. Each block is composed of 10 walkers realizing 2,000 VMC steps.

 

== Features under development ==

=== Properties ===
* Molecular Forces
* Moments (dipole, quadrupole,...)
* Electron density

=== Practical aspects ===
* Web interface for input and output

== Input file creation ==

The QMC=Chem input file can be created using the [{{SERVER}}/qmcchem_input.py web interface]. Upload a Q5Cost file or an output file from GAMESS, Gaussian or Molpro, and you will download the QMC=Chem input directory.

== Papers related to the QMC=Chem code ==

* [http://dx.doi.org/10.1002/jcc.23216 Quantum Monte Carlo for large chemical systems: Implementing efficient strategies for petascale platforms and beyond]
* [http://link.springer.com/chapter/10.1007/978-1-4614-0508-5_13 QMC=Chem: a quantum Monte Carlo program for large-scale simulations in chemistry at the petascale level and beyond]
* [http://link.springer.com/chapter/10.1007/978-1-4614-0508-5_13 Large-scale quantum Monte Carlo electronic structure calculations on the EGEE grid]

Quantum Monte Carlo for Chemistry @ Toulouse

2012-11-21T10:16:18Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* November 2012: Our results were presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Communication

2012-10-23T23:22:16Z

Scemama: /* TGCC/Curie on France 3 */

== Video files ==

=== Full beta-amyloid QMC simulation ===

Download video files here:
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.wmv .wmv format]
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mp4 .mp4 format]

or view on-line:

<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

=== Equip@Meso presentation ===

http://vod-flash.u-strasbg.fr:8080/canalc2/2012/1017_equipe_at_meso/20121018_11_scemama_equip@meso.mp4

=== TGCC/Curie on France 3 ===

http://www-hpc.cea.fr/fr/complexe/FRANCE_3_reportage_Curie_CEA_130312.swf

=== N2 animation with local energy graph ===

* [http://qmcchem.ups-tlse.fr/files/scemama/n2.gif Animated gif]
* [http://qmcchem.ups-tlse.fr/files/scemama/n2.mpeg MPEG]

== Pictures ==

=== QMC ===

[[File:Qmc.png|600px]]

Overview of a Quantum Monte Carlo simulation. Apart from the initialization and finalization step, all the processes are completely independent.

=== Beta-amyloid ===

[[File:peptide.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Different electron colors represent different time steps.

[[File:peptide_nozoom.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Electrons are repesented in gold, and a few
time steps in the past are represented as the footprints of the electrons.

[[File:alpha-beta.png|600px]]

The two beta-amyloid structures simulated on Curie in Dec 2011 (122 atoms, 434 electrons). The energy difference between these two structures was computed with QMC=Chem.

[[File:peptide_cu.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid fragment with a Copper atom.

Quantum Monte Carlo for Chemistry @ Toulouse

2012-10-22T07:33:32Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* November 2012: Our results will be presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the [http://nkl.cc.u-tokyo.ac.jp/VECPAR2012/ VECPAR 2012] Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2012-10-22T07:32:59Z

Scemama: /* News */

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* November 2012: Our results will be presented for the 5 years of GENCI (Paris).

* October 2012: QMC=Chem was presented at the [http://services-numeriques.unistra.fr/hpc.html Equip@Meso meeting] "Chimie et sciences de la vie : de la simulation numérique au HPC", Strasbourg

* July 2012: QMC=Chem was presented at the VECPAR 2012 Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2012-08-01T11:42:27Z

Scemama:

[[File:peptide_cu.png|350px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2012: QMC=Chem was presented at the VECPAR 2012 Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2012-08-01T11:41:59Z

Scemama:

[[File:peptide_cu.png|400px|right]]
This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2012: QMC=Chem was presented at the VECPAR 2012 Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

File:Peptide cu.png

2012-08-01T11:38:31Z

Scemama:

Communication

2012-08-01T11:38:10Z

Scemama: /* Beta-amyloid */

== Video files ==

=== Full beta-amyloid QMC simulation ===

Download video files here:
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.wmv .wmv format]
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mp4 .mp4 format]

or view on-line:

<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

=== TGCC/Curie on France 3 ===

http://www-hpc.cea.fr/fr/complexe/FRANCE_3_reportage_Curie_CEA_130312.swf

=== N2 animation with local energy graph ===

* [http://qmcchem.ups-tlse.fr/files/scemama/n2.gif Animated gif]
* [http://qmcchem.ups-tlse.fr/files/scemama/n2.mpeg MPEG]

== Pictures ==

=== QMC ===

[[File:Qmc.png|600px]]

Overview of a Quantum Monte Carlo simulation. Apart from the initialization and finalization step, all the processes are completely independent.

=== Beta-amyloid ===

[[File:peptide.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Different electron colors represent different time steps.

[[File:peptide_nozoom.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Electrons are repesented in gold, and a few
time steps in the past are represented as the footprints of the electrons.

[[File:alpha-beta.png|600px]]

The two beta-amyloid structures simulated on Curie in Dec 2011 (122 atoms, 434 electrons). The energy difference between these two structures was computed with QMC=Chem.

[[File:peptide_cu.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid fragment with a Copper atom.

Quantum Monte Carlo for Chemistry @ Toulouse

2012-08-01T11:37:01Z

Scemama: /* News */

This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* July 2012: QMC=Chem was presented at the VECPAR 2012 Conference, Kobe.

* June 2012: QMC=Chem was presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Communication

2012-06-11T16:43:11Z

Scemama: /* Video files */

== Video files ==

=== Full beta-amyloid QMC simulation ===

Download video files here:
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.wmv .wmv format]
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mp4 .mp4 format]

or view on-line:

<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

=== TGCC/Curie on France 3 ===

http://www-hpc.cea.fr/fr/complexe/FRANCE_3_reportage_Curie_CEA_130312.swf

=== N2 animation with local energy graph ===

* [http://qmcchem.ups-tlse.fr/files/scemama/n2.gif Animated gif]
* [http://qmcchem.ups-tlse.fr/files/scemama/n2.mpeg MPEG]

== Pictures ==

=== QMC ===

[[File:Qmc.png|600px]]

Overview of a Quantum Monte Carlo simulation. Apart from the initialization and finalization step, all the processes are completely independent.

=== Beta-amyloid ===

[[File:peptide.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Different electron colors represent different time steps.

[[File:peptide_nozoom.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Electrons are repesented in gold, and a few
time steps in the past are represented as the footprints of the electrons.

[[File:alpha-beta.png|600px]]

The two beta-amyloid structures simulated on Curie in Dec 2011 (122 atoms, 434 electrons). The energy difference between these two structures was computed with QMC=Chem.

Quantum Monte Carlo for Chemistry @ Toulouse

2012-05-29T12:17:26Z

Scemama: /* News */

This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* June 2012: QMC=Chem will be presented on the Intel booth at the International Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Quantum Monte Carlo for Chemistry @ Toulouse

2012-05-29T12:17:16Z

Scemama: /* News */

This website is devoted to the scientific and software
activities of the quantum Monte Carlo (QMC) group of Toulouse, France.
The grand objective of our project is to make of QMC an alternative and efficient tool for electronic structure in chemistry. Our group -- headed by Michel Caffarel -- is located at the
[http://www.lcpq.ups-tlse.fr Laboratoire de Chimie et Physique Quantiques], [http://www.cnrs.fr CNRS] and [http://www.ups-tlse.fr/ Université Paul Sabatier].
[[File:Anr.gif|100px|center]]

The QMC=Chem project is supported by the french Agence Nationale de la Recherche Scientifique (ANR) under Grant No. ANR2011 BS08 004 01


== News ==

* June 2012: QMC=Chem will be presented on the Intel booth at the International
Supercomputing Conference 2012, Hamburg.

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_FR.pdf '''PROPOSITION DE THESE (FINANCEE) A PARTIR DE SEPTEMBRE 2012''']

* [http://qmcchem.ups-tlse.fr/files/caffarel/these_ENG.pdf '''Ph.D. THESIS PROPOSAL''']

* March 2012: Beta-amyloid results presented for the Intel Xeon E5 release. [http://www.intel.com/content/dam/www/public/us/en/documents/case-studies/high-performance-xeon-e5-2680-genci-study.pdf Read more]

* Dec 2011: Two structures of a beta-amyloid involved in Alzheimer's disease were simulated on [http://www-hpc.cea.fr/fr/complexe/tgcc-curie.htm Curie (TGCC, France)] with QMC=Chem using up to 76 800 cores. 38.5% of the peak performance of the machine (960 GFlops/s) was obtained.

* Nov 2011: Performance of QMC=Chem presented at Supercomputing 2011 in [http://sc11.supercomputing.org/schedule/event_detail.php?evid=bof156 BoF session “1000 x 0 = 0. Single-node optimisation does matter.”]

== QMC in a few words ==
[[File:Qmc.png|400px|right]]
Quantum Monte Carlo (QMC) is a set of probabilistic approaches for solving the Schrödinger equation. In short, QMC consists in simulating the probabilities of quantum mechanics by using the probabilities of random walks (Brownian motion and its generalizations). During the simulations each electron is moved randomly and quantum averages are computed as ordinary averages.

<center>
<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

</center>

In practice, the major steps of a QMC simulation are as follows (See, Figure): 
Input: The molecular geometry, the number of electrons, and an approximate electronic trial wave function, ψT, obtained from a preliminary DFT or ab initio wave function-based calculation. 
 At each Monte Carlo step : The values of ψT, its gradient, and its Laplacian calculated at each spatial configuration (r1,r2, ...,rN). 
Output: Quantum averages as ordinary averages along stochastic trajectories. 

 Key property of QMC : Fully parallelizable.. This property could be critical in making QMC a successful approach.

[http://qmcchem.ups-tlse.fr/files/caffarel/qmc_eacm.pdf '''More about quantum Monte Carlo methods in chemistry here''']
 
__NOTOC__

== QMC an alternative to DFT or post-HF methods ? ==

In practice, both DFT and post-Hartree-Fock approaches and their numerous variants rely on solving (very) large linear systems using iterative algorithms, where the finite dimension of the eigenvectors may become very large and is limited in practice to a few billion of components due to the finite aspects of the hardware. Because of such a mathematical structure, present intensive simulations of computational chemistry are characterized by i) the need of important computational resources both in terms of CPU and central memory requirements, ii) massive I/O, and iii) unavoidable frequent communications between processors. As a consequence, the algorithms are by their very nature extremely difficult to parallelize. Although computational chemistry is very present on HPC platforms as illustrated above, it is difficult to envision how standard algorithms could take advantage in the near future of massively parallel platforms (exascale) and cloud computing.

=== DFT ===

Advantages:
<ul>
<li> The fully-correlated N-body electronic problem is replaced by
an effective one-body problem. Only approximation: Choice of the effective (exchange-correlation) potential,
a point leading to various levels of accuracy (local DFT, gradient-corrected DFT, hybrid DFT, etc...). One-body framework particularly attractive for interpreting electronic processes in a simple manner using one-electron pictures.
<li> Computational effort of DFT has a very good scaling, of order <math>O(N^3)</math> where N is the number of electrons.

<li> The various exchange-correlation potentials developped have now reached an accuracy allowing reasonable quantitative results, even for (very) large molecular systems.
</ul>
Limitation: Strong limitation of DFT: the error made is not controlled and there is no known procedure to reduce it in a systematic way.

=== Post-HF methods ===

Post-HF = expansion of the wave function over a sum of
antisymmetrized products of one-particle orbitals

Popular versions: MP2, MPn, CCSD(T), CI, MRCI, etc.

In contrast with DFT: Error much more easy to control but price to pay very high (defavorable scaling).

=== QMC: an alternative approach? ===

Advantages:
<ul>
<li> Method easy to implement and having a very favorable scaling, typically <math>O(N^3)</math>.

<li> Accurate total energies.

<li> Unlike DFT and post-HF methods, QMC ideally suited to High Performance Computing (HPC) (very modest central memory requirements, very limited input/output flows, codes perfectly parallelized).
</ul>
Present limitations:
<ul>
<li> The only systematic error left -the fixed-node error- may have an important impact when differences of energies are considered. The heavy compensation of errors at work in both DFT and post-HF schemes is much less effective in Fixed-Node QMC calculations.

<li> No general and robust algorithm for computing forces in QMC.

<li> No simple and systematic way of constructing complex trial wavefunctions of good quality without massive parameter reoptimizations. No "black-box" way for QMC.
</ul>

Communication

2012-04-24T16:40:02Z

Scemama: /* Video files */

== Video files ==

=== Full beta-amyloid QMC simulation ===

Download video files here:
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.wmv .wmv format]
* [http://qmcchem.ups-tlse.fr/files/scemama/1amb.mp4 .mp4 format]

or view on-line:

<html>
<table style="width:auto;"><tr><td><a href="https://picasaweb.google.com/lh/photo/7HFDA1WUJg2SgpWdsopVZdMTjNZETYmyPJy0liipFm0?feat=embedwebsite"><img src="https://lh5.googleusercontent.com/-tftU32oJ1c0/T2JhwNLCv0I/AAAAAAAAEO4/GfDH1K_v3LQ/s144/1amb.jpg" height="216" width="288" /></a></td></tr>
<tr><td style="font-family:arial,sans-serif; font-size:11px; text-align:center"><a href="http://www.pdb.org/pdb/explore/explore.do?structureId=1amb"> Amyloid Beta peptide</a>, 28 residues 
A stochastic trajectory for the 1731 electrons of systems. 
 Click on the image to see the animation. </td></tr></table>
</html>

=== TGCC/Curie on France 3 ===

http://www-hpc.cea.fr/fr/complexe/FRANCE_3_reportage_Curie_CEA_130312.swf

== Pictures ==

=== QMC ===

[[File:Qmc.png|600px]]

Overview of a Quantum Monte Carlo simulation. Apart from the initialization and finalization step, all the processes are completely independent.

=== Beta-amyloid ===

[[File:peptide.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Different electron colors represent different time steps.

[[File:peptide_nozoom.png|600px]]

Stochastic trajectories of electrons around the nuclei on the beta-amyloid simulated on Curie in Dec 2011. Electrons are repesented in gold, and a few
time steps in the past are represented as the footprints of the electrons.

[[File:alpha-beta.png|600px]]

The two beta-amyloid structures simulated on Curie in Dec 2011 (122 atoms, 434 electrons). The energy difference between these two structures was computed with QMC=Chem.