High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane

17 May 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dynamics to predict the equation of state properties of methane using high-dimensional neural network potentials (HDNNPs). We investigate two different strategies for generating training data: one based upon bulk representations using periodic cells, and another based upon clusters of molecules. We assess the accuracy of the trained potentials by predicting the equilibrium mass density for a wide range of thermodynamic conditions to characterize the liquid phase, supercritical fluid and gas phase, as well as the liquid-vapor coexistence curve. Our results show an excellent agreement with reference phase diagrams, with an average error below ∼ 2% for all studied phases. Moreover, we confirm the applicability of models trained on cluster data set for producing accurate and reliable results.


high-dimensional neural network potentials
molecular dynamics
equation of state

Supplementary materials

Supporting Information
The reference and predicted values of mass density of methane at different pressures and temperatures.


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