Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

08 May 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


molecular dynamics
machine learning
artificial neural network
radial distribution function
diffusion coefficient
ab initio molecular dynamics

Supplementary materials

argon si


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