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cond-mat.mtrl-sci2024★ 8 cited
A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements
Johann Lüder, Manabu Ihara, Sergei Manzhos
We present a machine-learned (ML) model of kinetic energy for orbital-free density functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of group III-…
cond-mat.mtrl-sci2024★ 7 cited
Machine learning the screening factor in the soft bond valence approach for rapid crystal structure estimation
Keisuke Kameda, Takaaki Ariga, Kazuma Ito +2
Development of new functional ceramics is important for several applications, including electrochemical batteries and fuel cells. Computational prescreening and selection of such m…