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cs.LG2022
Persistent homology-based descriptor for machine-learning potential of amorphous structures
Emi Minamitani, Ippei Obayashi, Koji Shimizu +1
High-accuracy prediction of the physical properties of amorphous materials is challenging in condensed-matter physics. A promising method to achieve this is machine-learning potent…
cond-mat.mtrl-sci2022
Using neural network potential to study point defect properties in multiple charge states of GaN with nitrogen vacancy
Koji Shimizu, Ying Dou, Elvis F. Arguelles +3
Investigation of charged defects is necessary to understand the properties of semiconductors. While density functional theory calculations can accurately describe the relevant phys…