239 citations · 488 across the 8 of their papers we have counts for
4 papers · 1 filter
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 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…
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 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…