most citedRepresenting Born effective charges with equivariant graph convolutional neural networks

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cond-mat.mtrl-sci2024★ 1 cited

Representing Born effective charges with equivariant graph convolutional neural networks

Alex Kutana, Koji Shimizu, Satoshi Watanabe +1

Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-…

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…