Accelerating the calculation of electron-phonon coupling by machine learning methods

Yang Zhong; Zhiguo Tao; Weibin Chu; Xingao Gong; Hongjun Xiang

Accelerating the calculation of electron-phonon coupling by machine learning methods

Computational Physics 2023-02-02 v1

Authors: Yang Zhong , Zhiguo Tao , Weibin Chu , Xingao Gong , Hongjun Xiang

Abstract

Electron-phonon coupling (EPC) plays an important role in many fundamental physical phenomena, but the high computational cost of the EPC matrix hinders the theoretical research on them. In this paper, an analytical formula is derived to calculate the EPC matrix in terms of the Hamiltonian and its gradient in the nonorthogonal atomic orbital bases. The recently-developed E(3) equivariant neural network is used to directly predict the Hamiltonian and its gradient needed by the formula, thus bypassing the expensive self-consistent iterations in DFT. The correctness of the proposed EPC calculation formula and the accuracy of the predicted EPC values of the network are illustrated by the tests on a water molecule and a MoS2 crystal.

Keywords

electron-phonon coupling computational physics density functional theory

Cite

@article{arxiv.2302.00439,
  title  = {Accelerating the calculation of electron-phonon coupling by machine learning methods},
  author = {Yang Zhong and Zhiguo Tao and Weibin Chu and Xingao Gong and Hongjun Xiang},
  journal= {arXiv preprint arXiv:2302.00439},
  year   = {2023}
}

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11 pages, 2 figures, 2 tables

Accelerating the calculation of electron-phonon coupling by machine learning methods

Abstract

Keywords

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