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physics.comp-ph2024
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…
cond-mat.mtrl-sci2024
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…
physics.comp-ph2024★ 58 cited
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…