activity
20202026
most citedDeep learning tight-binding approach for large-scale electronic simulations at finite temperatures with accuracy

53 citations · 83 across the 6 of their papers we have counts for

collaborators

6 papers

physics.chem-ph2026

Systematically Improvable Numerical Atomic Orbital Basis Using Contracted Truncated Spherical Waves

Yike Huang, Zuxin Jin, Linfeng Zhang +3

To solve the Kohn-Sham equation within the framework of density functional theory, we develop a scheme to construct numerical atomic orbital (NAO) basis sets by contracting truncat…

cond-mat.mtrl-sci2025★ 1 cited

ABACUS: An Electronic Structure Analysis Package for the AI Era

Weiqing Zhou, Daye Zheng, Qianrui Liu +55

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It…

cond-mat.mes-hall2024★ 1 cited

Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistors

Jijie Zou, Zhanghao Zhouyin, Dongying Lin +4

Quantum transport simulations are essential for understanding and designing nanoelectronic devices, yet the long-standing trade-off between accuracy and computational efficiency ha…

cond-mat.mtrl-sci2024★ 1 cited

Learning local equivariant representations for quantum operators

Zhanghao Zhouyin, Zixi Gan, MingKang Liu +3

Predicting quantum operator matrices such as Hamiltonian, overlap, and density matrices in the density functional theory (DFT) framework is crucial for material science. Current me…

cond-mat.mtrl-sci2023★ 53 cited

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with accuracy

Qiangqiang Gu, Zhanghao Zhouyin, Shishir Kumar Pandey +3

Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the framework due to its computational inte…

cond-mat.mtrl-sci2020★ 27 cited

Neural network representation of electronic structure from molecular dynamics

Qiangqiang Gu, Linfeng Zhang, Ji Feng

Despite their rich information content, electronic structure data amassed at high volumes in molecular dynamics simulations are generally under-utilized. We introduce…