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

초록

The 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.

키워드