用等变图卷积神经网络表示玻恩有效电荷

Alex Kutana, Koji Shimizu, Satoshi Watanabe, Ryoji Asahi
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引用次数: 0

摘要

图卷积神经网络在材料特性的机器学习中发挥了重要作用。在表示张量属性时,物理信息网络的权重和描述符必须遵守一定的变换规则,以确保属性与参考框架的选择无关。在这里,我们使用一个后向图卷积神经网络来明确编码这些属性。通过使用等变权重和描述符,该网络自始至终尊重晶体的旋转对称性,并提供目标值的张量输出。实验证明了该网络在不同材料(包括perovskite氧化物、Li3PO4和ZrO2)中原子Born有效电荷张量的应用,并获得了良好的性能和泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Representing Born effective charges with equivariant graph convolutional neural networks
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 choice of the reference frame. Here we explicitly encode such properties using an equivariant graph convolutional neural network. The network respects rotational symmetries of the crystal throughout by using equivariant weights and descriptors and provides a tensorial output of the target value. Applications to tensors of atomic Born effective charges in diverse materials including perovskite oxides, Li3PO4, and ZrO2, are demonstrated, and good performance and generalization ability is obtained.
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