239 citations · 488 across the 8 of their papers we have counts for
10 papers
Nonlinear optical responses of semiconducting graphene nanoribbons: Strong constraint from a hidden symmetry
Jiawei Ruan, Weichen Tang, Yang-Hao Chan +3
Nonlinear optical (NLO) responses are powerful tools for probing material structures, as they are highly sensitive to spatial symmetries. For instance, it is well known that having…
Deep Learning GW Quasiparticle Hamiltonians for Many-Body Excited-State Electronic Structure at Scale
Xiaoxun Gong, Zechen Tang, Woochang Kim +4
Accurate quasiparticle electronic structures are the foundation for understanding excited-state properties of materials and explaining optoelectronic, quantum, and transport phenom…
DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations
Yang Li, Yanzhen Wang, Boheng Zhao +15
In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and…
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…
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…
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…
Efficient hybrid density functional calculation by deep learning
Zechen Tang, He Li, Peize Lin +7
Hybrid density functional calculation is indispensable to accurate description of electronic structure, whereas the formidable computational cost restricts its broad application. H…
Deep-learning electronic-structure calculation of magnetic superstructures
He Li, Zechen Tang, Xiaoxun Gong +3
Ab initio study of magnetic superstructures (e.g., magnetic skyrmion) is indispensable to the research of novel materials but bottlenecked by its formidable computational cost. For…
General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
Xiaoxun Gong, He Li, Nianlong Zou +3
Combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating…
Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
He Li, Zun Wang, Nianlong Zou +5
The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural n…