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