高效计算电子-声子耦合的机器学习工具。

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Nature computational science Pub Date : 2024-08-08 DOI:10.1038/s43588-024-00680-x
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引用次数: 0

摘要

建立了一个机器学习框架,利用基于原子轨道的哈密顿矩阵和等变图神经网络预测的梯度来计算电子-声子耦合(EPC)。这种方法将计算速度提高了几个数量级,从而能够利用高精度函数预测复杂系统的 EPC 相关特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A machine learning tool to efficiently calculate electron–phonon coupling
A machine learning framework that uses atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network is established to calculate electron–phonon coupling (EPC). This approach accelerates the calculations by several orders of magnitude, enabling EPC-related properties to be predicted for complex systems using highly accurate functionals.
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