scholarly journals Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

2022 ◽  
Author(s):  
Chenghan Li ◽  
Gregory A. Voth
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Path Integral ◽  
Ab Initio Molecular Dynamics

scholarly journals Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

2021 ◽  
Author(s):  
Chenghan Li ◽  
Gregory A. Voth
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Path Integral ◽  
Computational Cost ◽  
Ab Initio Molecular Dynamics ◽  
Direct Path ◽  
Widespread Application ◽  
Path Integral Representation ◽  
Order Of Magnitude

Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest scale systems. The situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a two-order-of-magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

scholarly journals Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

2022 ◽  
Author(s):  
Chenghan Li ◽  
Gregory A. Voth
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Path Integral ◽  
Computational Cost ◽  
Computational Approach ◽  
Ab Initio Molecular Dynamics ◽  
Direct Path ◽  
Widespread Application ◽  
Path Integral Representation

Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest scale systems. The situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a two-orders-of-magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

Ab initio molecular dynamics study of crystal hydrates of HCl including path integral results

1997 ◽  
Vol 106 ◽  
pp. 273-289 ◽  
Author(s):  
Tycho von Rosenvinge ◽  
Mark E. Tuckerman ◽  
Michael L. Klein
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Path Integral ◽  
Ab Initio Molecular Dynamics

scholarly journals Methanol Carbonylation over Acid Mordenite: Insights from Ab Initio Molecular Dynamics and Machine Learning Thermodynamic Perturbation Theory

2021 ◽  
Author(s):  
Monika Gešvandtnerová ◽  
Dario Rocca ◽  
Tomas Bucko
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Thermodynamic Perturbation Theory ◽  
Dynamics Simulations ◽  
Thermodynamic Perturbation

<div>In this work we present a detailed \textit{ab initio} study of the carbonylation reaction of methoxy groups in the zeolite mordenite, as it is the rate determining step in a series of elementary reactions leading to ethanol. </div><div>For the first time we employ full molecular dynamics simulations to evaluate free energies of activation for the reactions in side pockets and main channels. Results show that the reaction in the side pocket is preferred and, when dispersion interactions are taken into account, this preference becomes even stronger. This conclusion is confirmed using multiple levels of density functional theory approximations with (PBE-D2, PBE-MBD, and vdW-DF2-B86R) or without (PBE, HSE06) dispersion corrections. These calculations, that in principle would require several demanding molecular dynamics simulations, were made possible at a minimal computational cost by using a newly developed approach that combines thermodynamic perturbation theory with machine learning.</div>

scholarly journals Machine learning for analysing ab initio molecular dynamics simulations

2020 ◽  
Vol 1412 ◽  
pp. 042003 ◽  
Author(s):  
Florian Häse ◽  
Ignacio Fdez. Galván ◽  
Alán Aspuru-Guzik ◽  
Roland Lindh ◽  
Morgane Vacher
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Ab Initio Molecular Dynamics ◽  
Dynamics Simulations

scholarly journals How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry

2019 ◽  
Vol 10 (8) ◽  
pp. 2298-2307 ◽  
Author(s):  
Florian Häse ◽  
Ignacio Fdez. Galván ◽  
Alán Aspuru-Guzik ◽  
Roland Lindh ◽  
Morgane Vacher
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Conceptual Understanding ◽  
Ab Initio Molecular Dynamics ◽  
Learning Models ◽  
Dynamics Simulations ◽  
Molecular Dynamics Results ◽  
Machine Learning Models

Machine learning models, trained to reproduce molecular dynamics results, help interpreting simulations and extracting new understanding of chemistry.

scholarly journals Methanol Carbonylation over Acid Mordenite: Insights from Ab Initio Molecular Dynamics and Machine Learning Thermodynamic Perturbation Theory

2021 ◽  
Author(s):  
Monika Gešvandtnerová ◽  
Dario Rocca ◽  
Tomas Bucko
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Thermodynamic Perturbation Theory ◽  
Dynamics Simulations ◽  
Thermodynamic Perturbation

<div>In this work we present a detailed \textit{ab initio} study of the carbonylation reaction of methoxy groups in the zeolite mordenite, as it is the rate determining step in a series of elementary reactions leading to ethanol. </div><div>For the first time we employ full molecular dynamics simulations to evaluate free energies of activation for the reactions in side pockets and main channels. Results show that the reaction in the side pocket is preferred and, when dispersion interactions are taken into account, this preference becomes even stronger. This conclusion is confirmed using multiple levels of density functional theory approximations with (PBE-D2, PBE-MBD, and vdW-DF2-B86R) or without (PBE, HSE06) dispersion corrections. These calculations, that in principle would require several demanding molecular dynamics simulations, were made possible at a minimal computational cost by using a newly developed approach that combines thermodynamic perturbation theory with machine learning.</div>

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