Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning

Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi +3

We propose a scheme for {\it ab initio} configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventio…