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Physical Review Letters
|
March 15, 2024
Deep-Learning Density Functional Perturbation Theory
He Li, Zechen Tang, Jingheng Fu, et al.
Nature Communications
|
October 11, 2024
A deep equivariant neural network approach for efficient hybrid density functional calculations
Zechen Tang, He Li, Peize Lin, et al.
Physical Review Letters
|
August 30, 2024
Neural-network Density Functional Theory Based on Variational Energy Minimization
Yang Li, Zechen Tang, Zezhou Chen, et al.
Nature Computational Science
|
December 22, 2025
Deep-learning electronic structure calculations
Zechen Tang, Haoxiang Chen, Yang Li, et al.
Science Bulletin
|
June 28, 2024
Universal materials model of deep-learning density functional theory Hamiltonian
Yuxiang Wang, Yang Li, Zechen Tang, et al.
The Journal of Chemical Physics
|
November 20, 2025
ABACUS: An electronic structure analysis package for the AI era
Weiqing Zhou, Daye Zheng, Qianrui Liu, et al.
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Search research articles
Search
Showing results (11-20 of 16) with videos related to
Sort By:
Page
of 2
You have reached the last page of results.
This site can display upto 16 results.
Physical Review Letters
|
March 15, 2024
Deep-Learning Density Functional Perturbation Theory
He Li, Zechen Tang, Jingheng Fu, et al.
Nature Communications
|
October 11, 2024
A deep equivariant neural network approach for efficient hybrid density functional calculations
Zechen Tang, He Li, Peize Lin, et al.
Physical Review Letters
|
August 30, 2024
Neural-network Density Functional Theory Based on Variational Energy Minimization
Yang Li, Zechen Tang, Zezhou Chen, et al.
Nature Computational Science
|
December 22, 2025
Deep-learning electronic structure calculations
Zechen Tang, Haoxiang Chen, Yang Li, et al.
Science Bulletin
|
June 28, 2024
Universal materials model of deep-learning density functional theory Hamiltonian
Yuxiang Wang, Yang Li, Zechen Tang, et al.
The Journal of Chemical Physics
|
November 20, 2025
ABACUS: An electronic structure analysis package for the AI era
Weiqing Zhou, Daye Zheng, Qianrui Liu, et al.
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of 2