239 citations · 564 across the 15 of their papers we have counts for
11 papers · 1 filter
Deep Band Crossings Enhanced Nonlinear Optical Effects
Nianlong Zou, He Li, Meng Ye +8
Nonlinear optical (NLO) effects in materials with band crossings have attracted significant research interests due to the divergent band geometric quantities around these crossings…
Deep learning density functional theory Hamiltonian in real space
Zilong Yuan, Zechen Tang, Honggeng Tao +11
Deep learning electronic structures from ab initio calculations holds great potential to revolutionize computational materials studies. While existing methods proved success in dee…
Ab initio study of Z(N) = 6 magicity
H. Li, H. J. Ong, D. Fang +7
The existence of magic numbers of protons and neutrons in nuclei is essential for understanding nuclear structure and fundamental nuclear forces. Over decades, researchers have con…
Improving density matrix electronic structure method by deep learning
Zechen Tang, Nianlong Zou, He Li +10
The combination of deep learning and ab initio materials calculations is emerging as a trending frontier of materials science research, with deep-learning density functional theory…
Universal materials model of deep-learning density functional theory Hamiltonian
Yuxiang Wang, Yang Li, Zechen Tang +14
Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challeng…
Direct ab initio calculation of the He nuclear electric dipole polarizability
Peng Yin, Andrey M. Shirokov, Pieter Maris +5
The calculation of nuclear electromagnetic sum rules by directly diagonalizing the nuclear Hamiltonian in a large basis is numerically challenging and has not been performed for $A…
Deep-Learning Database of Density Functional Theory Hamiltonians for Twisted Materials
Ting Bao, Runzhang Xu, He Li +5
Moiré-twisted materials have garnered significant research interest due to their distinctive properties and intriguing physics. However, conducting first-principles studies on such…
Neural-network Density Functional Theory Based on Variational Energy Minimization
Yang Li, Zechen Tang, Zezhou Chen +7
Deep-learning density functional theory (DFT) shows great promise to significantly accelerate material discovery and potentially revolutionize materials research. However, current…
Equivariant Neural Network Force Fields for Magnetic Materials
Zilong Yuan, Zhiming Xu, He Li +6
Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is stil…
Deep-learning density functional perturbation theory
He Li, Zechen Tang, Jingheng Fu +5
Calculating perturbation response properties of materials from first principles provides a vital link between theory and experiment, but is bottlenecked by the high computational c…
DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer
Yuxiang Wang, He Li, Zechen Tang +6
Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architec…