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Research
Research Overview
My research interest includes generative models, geometric deep learning, and leveraging AI techniques for solving scientific problems, including but not limited to quantum chemistry acceleration (especially for density functional theory), molecular structure prediction.
Research Interest
Selected Publications
(†equal contribution, *corresponding authors)
Quantum Chemistry
Haoran Li, Bilin Gui, He Zhang*, Le Yang*, Hao-Sen Chen. Accelerated Stable Structure Prediction of Li-Intercalated Bilayer Graphene Using a Data-Efficient Deep Learning Framework. npj Computational Materials, 2026
He Zhang†, Siyuan Liu†, Jiacheng You, Chang Liu*, Shuxin Zheng*, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao*. Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning. Nature Computational Science, 2024.
He Zhang, Chang Liu*, Zun Wang, Xinran Wei, Siyuan Liu, Nanning Zheng*, Bin Shao, Tie-Yan Liu. Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction. International Conference on Machine Learning, 2024.
Computatioal Biology
Hao Zhang, He Zhang*, Miao Kang, Kaipeng Zhang, Tao Yang, Nanning Zheng*. Functional Locality–Aligned Learning Reveals Structure–Function Causality in Enzyme Kinetics. preprint, 2026
He Zhang†, Fusong Ju†, Jianwei Zhu, Liang He, Bin Shao*, Nanning Zheng*, Tie-Yan Liu. Coevolution transformer for protein contact prediction. Advances in Neural Information Processing Systems, 2021.
Weitao Du*†, He Zhang†, Yuanqi Du, Qi Meng*, Wei Chen, Nanning Zheng, Bin Shao, Tie-Yan Liu. SE(3) equivariant graph neural networks with complete local frames. International Conference on Machine Learning, 2022.
Generative Models
Weitao Du†, He Zhang†, Tao Yang†, Yuanqi Du†. A flexible diffusion model. International Conference on Machine Learning, 2023.
A fuller list of papers is on Google Scholar.
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