The accurate estimation of the third virial coefficients for helium using three‐body neural network potentials

Abstract

AbstractThe description of many‐body interactions is one of challenging problems in molecular dynamics simulations. Recently, neural network potentials have been spotlighted as an approach to describe many‐body interactions. In this study, we obtain the neural network potentials for three‐body interactions of helium using a deep learning method. We perform quantum calculations to obtain single point energies for helium trimers and obtain the neural network potentials for three‐body interactions by performing a deep learning method. In order to test the validity of the neural network three‐body interactions, we perform Mayer‐sampling Monte Carlo simulations and calculate third virial coefficients for helium. We show that the third virial coefficients obtained from three‐body neural network potentials are more accurate than those obtained from two‐body neural network potentials. The deep learning method in our study would be extended to obtain the high‐order virial coefficients for complex molecules.