title: Artificial neural network for the configuration problem in solids.
abstract: A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R2= 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R2= 0.95 and MAD = 0.13 eV.
authors.affiliation: Graduate School of EEWS, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
authors.forename: Hyunjun
authors.initials: H
authors.lastname: Ji
authors.affiliation: Graduate School of EEWS, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
authors.forename: Yousung
authors.initials: Y
authors.lastname: Jung