Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide
Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide
Abstract Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling of complex materials with near first-principles accuracy. For molecular dynamics simulations, accurate energies and interatomic forces are a prerequisite, but training ANN potentials simultaneously on energies and forces from electronic structure calculations is computationally demanding. Here, we introduce …