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

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 by using high-dimensional neural network
potentials (HDNNPs). We investigate two different strategies for generating
training data: one strategy based upon bulk representations using
periodic cells and another strategy 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 sets for
producing accurate and reliable results.