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 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 Ab initio molecular dynamics studies of formic acid dimer colliding with liquid water

2018 ◽  
Vol 20 (36) ◽  
pp. 23717-23725 ◽  
Author(s):  
Vesa Hänninen ◽  
Garold Murdachaew ◽  
Gilbert M. Nathanson ◽  
R. Benny Gerber ◽  
Lauri Halonen
Keyword(s):  
Molecular Dynamics ◽  
Density Functional Theory ◽  
Formic Acid ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Liquid Water ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Functional Theory ◽  
Dynamics Simulations

Ab initio molecular dynamics simulations of formic acid (FA) dimer colliding with liquid water at 300 K have been performed using density functional theory.

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 Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

2020 ◽  
Author(s):  
Punyaslok Pattnaik ◽  
Shampa Raghunathan ◽  
Tarun Kalluri ◽  
Prabhakar Bhimalapuram ◽  
C. V. Jawahar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Time Scales ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Experimental Conditions ◽  
The Neural Network ◽  
Dynamics Simulations

<p>The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.</p>

Dissociation mechanism from highly charged bromophenol: ab initio molecular dynamics simulations

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Satoshi Ohmura ◽  
Kiyonobu Nagaya ◽  
Fuyuki Shimojo ◽  
Makoto Yao
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Electronic State ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Electronic Temperature ◽  
Multi Stage ◽  
Dissociation Mechanisms ◽  
Dynamics Simulations

AbstractDissociation mechanisms are studied by ab initio molecular dynamics simulations based on density functional theory for the highly charged bromophenol (C6H4OHBr)n+ (n ≤ 10) in the ground electronic state and in an electronic state which has a high electronic temperature Te characterized by Fermi–Dirac distribution. In the case of the ground state, the dissociation occurs through a sequential multi-stage process. At times shorter than 20 fs after the molecule is charged, hydrogens are dissociated from the molecule and, subsequently, the carbon ring breaks at about 150 fs In the case of an electronic state with high Te, the mechanism changes from a sequential dissociation process to a simultaneous process occurring at Te > 5 eV. To estimate the charge transfer time in a molecular bromide parent ion with +6 charge, which is generated through Auger cascades, we also performed nonadiabatic quantum-mechanical molecular dynamics (NAQMD) simulations that include the effects of nonadiabatic electronic transition with a surface-hopping approach.

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