Method for predicting potential energy of multicomponent materials

Abstract

The present disclosure provides a method for predicting a potential energy of a multicomponent material. The method includes encoding a structural information of a multicomponent material in which eigenvector values for each element are calculated using an atom-centered symmetry function (ACSF) of a structural information of atoms constituting the multicomponent material. The method also includes calculating a bond order potential (BOP) parameter for each element using the calculated eigenvector value as an input value and using a different artificial neural network model for each type of atom to construct a physics-informed artificial neural network (PINN) model. The method also includes calculating a bond order potential value for each element to construct a potential energy surface (PES). The method also includes performing structural optimization or physical property prediction of the multicomponent material using a potential energy surface.

Dependencies are inferred from the claim text.