collaborators

7 papers

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

Neural-network Density Functional Theory Based on Variational Energy Minimization

Yang Li, Zechen Tang, Zezhou Chen +7

Deep-learning density functional theory (DFT) shows great promise to significantly accelerate material discovery and potentially revolutionize materials research. However, current…

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…

nucl-th2024

Direct ab initio calculation of the He nuclear electric dipole polarizability

Peng Yin, Andrey M. Shirokov, Pieter Maris +5

The calculation of nuclear electromagnetic sum rules by directly diagonalizing the nuclear Hamiltonian in a large basis is numerically challenging and has not been performed for $A…

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

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…