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20182026
most citedDeep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation

239 citations · 633 across the 17 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cond-mat.str-el2024★ 16 cited

Correlated topological flat bands in rhombohedral graphite

Hongyun Zhang, Qian Li, Michael G. Scheer +22

Flat bands and nontrivial topological physics are two important topics of condensed matter physics. With a unique stacking configuration analogous to the Su-Schrieffer-Heeger (SSH)…

cond-mat.mes-hall2024

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…

physics.comp-ph2024

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…

physics.comp-ph2024★ 44 cited

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…

cs.LG2024★ 1 cited

Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models

Zun Wang, Chang Liu, Nianlong Zou +5

In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Mode…

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…