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

12 citations · 12 across the 1 of their papers we have counts for

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1 paper

physics.comp-ph2020★ 12 cited

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…