He Li (李贺)
He Li (李贺)
Other namesH. Li, He Li
Institute for Advanced Study, Tsinghua University
Verified email at - Homepage
Cited by
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Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
H Li, Z Wang, N Zou, M Ye, R Xu, X Gong, W Duan, Y Xu
Nature Computational Science 2 (6), 367-377, 2022
General framework for E (3)-equivariant neural network representation of density functional theory Hamiltonian
X Gong, H Li, N Zou, R Xu, W Duan, Y Xu
Nature Communications 14 (1), 2848, 2023
Deep-learning electronic-structure calculation of magnetic superstructures
H Li, Z Tang, X Gong, N Zou, W Duan, Y Xu
Nature Computational Science 3 (4), 321-327, 2023
Deep-learning density functional perturbation theory
H Li, Z Tang, J Fu, WH Dong, N Zou, X Gong, W Duan, Y Xu
Physical Review Letters 132 (9), 096401, 2024
DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer
Y Wang, H Li, Z Tang, H Tao, Y Wang, Z Yuan, Z Chen, W Duan, Y Xu
arXiv preprint arXiv:2401.17015, 2024
Equivariant neural network force fields for magnetic materials
Z Yuan, Z Xu, H Li, X Cheng, H Tao, Z Tang, Z Zhou, W Duan, Y Xu
Quantum Frontiers 3 (1), 8, 2024
Efficient hybrid density functional calculation by deep learning
Z Tang, H Li, P Lin, X Gong, G Jin, L He, H Jiang, X Ren, W Duan, Y Xu
arXiv preprint arXiv:2302.08221, 2023
Neural-network density functional theory
Y Li, Z Tang, Z Chen, M Sun, B Zhao, H Li, H Tao, Z Yuan, W Duan, Y Xu
arXiv preprint arXiv:2403.11287, 2024
Ab initio artificial intelligence: Future research of Materials Genome Initiative
H Li, Y Xu, W Duan
Materials Genome Engineering Advances 1 (2), e16, 2023
Universal materials model of deep-learning density functional theory Hamiltonian
Y Wang, Y Li, Z Tang, H Li, Z Yuan, H Tao, N Zou, T Bao, X Liang, Z Chen, ...
Science Bulletin, 2024
Deep-Learning Database of Density Functional Theory Hamiltonians for Twisted Materials
T Bao, R Xu, H Li, X Gong, Z Tang, J Fu, W Duan, Y Xu
arXiv preprint arXiv:2404.06449, 2024
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