309 citations · 1.3k across the 28 of their papers we have counts for
6 papers · 1 filter
Universal Machine Learning Kohn-Sham Hamiltonian for Materials
Yang Zhong, Hongyu Yu, Jihui Yang +3
While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persi…
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…
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…
Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids
Yang Zhong, Hongyu Yu, Mao Su +2
Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices…
Spin-Dependent Graph Neural Network Potential for Magnetic Materials
Hongyu Yu, Yang Zhong, Liangliang Hong +4
The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potenti…
A parallel orbital-updating based plane-wave basis method for electronic structure calculations
Yan Pan, Xiaoying Dai, Stefano de Gironcoli +3
Motivated by the recently proposed parallel orbital-updating approach in real space method, we propose a parallel orbital-updating based plane-wave basis method for electronic stru…