309 citations · 1.3k across the 28 of their papers we have counts for
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physics.comp-ph2023
Transferable Machine Learning Approach for Predicting Electronic Structures of Charged Defects
Yuxing Ma, Yang Zhong, Yu Hongyu +2
The study of the electronic properties of charged defects is crucial for our understanding of various electrical properties of materials. However, the high computational cost of de…
cond-mat.mtrl-sci2023★ 1 cited
Accelerating the electronic-structure calculation of magnetic systems by equivariant neural networks
Yang Zhong, Binhua Zhang, Hongyu Yu +2
Complex spin-spin interactions in magnets can often lead to magnetic superlattices with complex local magnetic arrangements, and many of the magnetic superlattices have been found…
physics.comp-ph2023★ 2 cited
Accelerating the calculation of electron-phonon coupling by machine learning methods
Yang Zhong, Zhiguo Tao, Weibin Chu +2
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…