Adaptive machine learning framework to accelerate ab initio molecular dynamics

2014 ◽  
Vol 115 (16) ◽  
pp. 1074-1083 ◽  
Author(s):  
Venkatesh Botu ◽  
Rampi Ramprasad
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Ab Initio Molecular Dynamics ◽  
Learning Framework

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 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 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 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.

Combustion Driven by Fragment-based Ab Initio Molecular Dynamics Simulation

2019 ◽  
Author(s):  
Liqun Cao ◽  
Jinzhe Zeng ◽  
Mingyuan Xu ◽  
Chih-Hao Chin ◽  
Tong Zhu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Large Scale ◽  
Methane Combustion ◽  
Combustion Method ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Computational Time ◽  
Combustion Mechanism ◽  
Daily Lives

Combustion is a kind of important reaction that affects people's daily lives and the development of aerospace. Exploring the reaction mechanism contributes to the understanding of combustion and the more efficient use of fuels. Ab initio quantum mechanical (QM) calculation is precise but limited by its computational time for large-scale systems. In order to carry out reactive molecular dynamics (MD) simulation for combustion accurately and quickly, we develop the MFCC-combustion method in this study, which calculates the interaction between atoms using QM method at the level of MN15/6-31G(d). Each molecule in systems is treated as a fragment, and when the distance between any two atoms in different molecules is greater than 3.5 Å, a new fragment involved two molecules is produced in order to consider the two-body interaction. The deviations of MFCC-combustion from full system calculations are within a few kcal/mol, and the result clearly shows that the calculated energies of the different systems using MFCC-combustion are close to converging after the distance thresholds are larger than 3.5 Å for the two-body QM interactions. The methane combustion was studied with the MFCC-combustion method to explore the combustion mechanism of the methane-oxygen system.

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