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>

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>

Neural network potential for Zr-Rh system by machine learning

2021 ◽  
Author(s):  
Kun Xie ◽  
Chong Qiao ◽  
Hong Shen ◽  
Riyi Yang ◽  
Ming Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mechanical Property ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Structural Features ◽  
Ab Initio Molecular Dynamics ◽  
The Neural Network ◽  
Dynamics Simulations ◽  
Good Agreement

Abstract Zr-Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr-Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. The results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr77Rh23. Our study lays a foundation for exploring the complex structures in amorphous Zr77Rh23, which is of great significance for the design and practical application.

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>

Catalyzed β scission of a carbenium ion III — Scission observed in ab initio molecular dynamics simulations

2009 ◽  
Vol 87 (10) ◽  
pp. 1512-1520 ◽  
Author(s):  
Greg M. Berner ◽  
Allan L. L. East
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Density Functional ◽  
Time Windows ◽  
Three Dimensional ◽  
Ab Initio Molecular Dynamics ◽  
Catalytic Systems ◽  
Carbenium Ions ◽  
Carbenium Ion ◽  
Dynamics Simulations

The β scission (cracking) of branched carbenium ions have been observed in molecular dynamics simulations, possibly for the first time. Simulations were performed with molecular dynamics based on PW91 density functional theory, and which included three-dimensional periodic boundary replication of the unit cell to mimic long-range bulk effects. A rising-temperature algorithm was used to encourage reaction within the narrow time windows (∼10 ps) of the simulations. Twenty-eight simulations were performed, featuring alkyl ions in three different catalytic systems: the ionic liquid, [(C5H5NH+)5(Al2Cl7−)6]−, the chabazite zeolite, [AlSi23O48]−, and the chabazite zeolite, [Al4Si20O45(OH)3]−. Twenty-four runs began with unbranched sec-n-alkyl ions, but only one exhibited β scission, and only after branching to a tertiary ion and under extreme heating. In contrast, the four simulations that began with branched alkyl ions were all successful in demonstrating β scission at lower temperatures: 2,4,4-trimethyl-2-pentyl ion and 2,4-dimethyl-2-hexyl ion in each of the first two catalysts. The lifetimes of desorbed alkyl ions in the chabazite models were < 5 ps at 1000–1500 K. The β scission results support the classical Weitkamp et al. ( Appl. Catal. 1983, 8, 123 ) mechanism over the nonclassical Sie ( Ind. Eng. Chem. Res. 1992, 31, 1881 ) and the chemisorping Kazansky et al. ( J. Catal. 1989, 119, 108 ) mechanisms.

Ab-Initio Molecular Dynamics Simulations and Calculations of Spectroscopic Parameters in Hydrogen-Bonding Liquids in Confinement (Project 8)

2018 ◽  
Vol 232 (7-8) ◽  
pp. 973-987 ◽  
Author(s):  
Daniel Sebastiani
Keyword(s):  
Molecular Dynamics ◽  
Hydrogen Bonding ◽  
Molecular Dynamics Simulations ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Aqueous Mixtures ◽  
Functional Theory ◽  
Spatially Resolved ◽  
Local Strength ◽  
Dynamics Simulations

Abstract We investigate the effect of several nanoscale confinements on structural and dynamical properties of liquid water and binary aqueous mixtures. By means of molecular dynamics simulations based on density functional theory and atomistic force fields. Our main focus is on the dependence on the structure and the hydrogen-bonding-network of the liquids near the confinement interface at atomistic resolution. As a complementary aspect, spatially resolved profiles of the proton NMR chemical shift values are used to quantify the local strength of the hydrogen-bond-network.

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.

Sign in / Sign up

Export Citation Format

Share Document