Physically informed artificial neural networks for atomistic modeling of materials

Abstract

Traditional machine learning potentials suffer from poor transferability to unknown structures. Here the authors present an approach to improve the transferability of machine-learning potentials by including information on the physical nature of interatomic bonding.