Representing Born effective charges with equivariant graph convolutional neural networks

Representing Born effective charges with equivariant graph convolutional neural networks

Abstract Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the independence of the property on the choic...

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Main Authors: Alex Kutana, Koji Shimizu, Satoshi Watanabe, Ryoji Asahi
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Equivariant graph convolutional neural networks
Physics-informed neural networks
Tensor of atomic Born effective charges
Linear response
Oxides
Online Access:https://doi.org/10.1038/s41598-025-01250-5
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https://doi.org/10.1038/s41598-025-01250-5

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