An efficient adaptive mesh redistribution method for a non-linear Dirac equation. - Han Wang 006; Huazhong Tang (J. Comput. Phys., 2007)

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics. - Linfeng Zhang 002; Jiequn Han; Han Wang 006; Roberto Car; Weinan E (CoRR, 2017)

Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic Methodology. - Linfeng Zhang 002; Han Wang 006; Weinan E (CoRR, 2017)

Parallel 3-dim fast Fourier transforms with load balancing of the plane waves. - Xingyu Gao 003; Zeyao Mo; Jun Fang; Haifeng Song 003; Han Wang 006 (Comput. Phys. Commun., 2017)

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems. - Linfeng Zhang 002; Jiequn Han; Han Wang 006; Wissam Saidi; Roberto Car; Weinan E (NeurIPS, 2018)

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation. - Linfeng Zhang 002; De-Ye Lin; Han Wang 006; Roberto Car; Weinan E (CoRR, 2018)

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. - Han Wang 006; Linfeng Zhang 002; Jiequn Han; Weinan E (Comput. Phys. Commun., 2018)

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning. - Weile Jia; Han Wang 006; Mohan Chen 002; Denghui Lu; Lin Lin 001; Roberto Car; Weinan E; Linfeng Zhang 002 (SC, 2020)

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy. - Denghui Lu; Han Wang 006; Mohan Chen 002; Jiduan Liu; Lin Lin 001; Roberto Car; Weinan E; Weile Jia; Linfeng Zhang 002 (CoRR, 2020)

DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory. - Yixiao Chen; Linfeng Zhang 002; Han Wang 006; Weinan E (CoRR, 2020)

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models. - Yuzhi Zhang; Haidi Wang; Weijie Chen 003; Jinzhe Zeng; Linfeng Zhang 002; Han Wang 006; Weinan E (Comput. Phys. Commun., 2020)

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy. - Denghui Lu; Han Wang 006; Mohan Chen 002; Lin Lin 001; Roberto Car; Weinan E; Weile Jia; Linfeng Zhang 002 (Comput. Phys. Commun., 2021)

Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms. - Zhuoqiang Guo; Denghui Lu; Yujin Yan; Siyu Hu; Rongrong Liu; Guangming Tan; Ninghui Sun; Wanrun Jiang; Lijun Liu; Yixiao Chen; Linfeng Zhang 002; Mohan Chen 002; et al. (PPoPP, 2022)

Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms. - Zhuoqiang Guo; Denghui Lu; Yujin Yan; Siyu Hu; Rongrong Liu; Guangming Tan; Ninghui Sun; Wanrun Jiang; Lijun Liu; Yixiao Chen; Linfeng Zhang 002; Mohan Chen 002; et al. (CoRR, 2022)

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials. - Wenfei Li; Qi Ou; Yixiao Chen; Yu Cao; Renxi Liu; Chunyi Zhang; Daye Zheng; Chun Cai; Xifan Wu; Han Wang 006; Mohan Chen 002; Linfeng Zhang 002 (CoRR, 2022)

DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation. - Duo Zhang; Hangrui Bi; Fu-Zhi Dai; Wanrun Jiang; Linfeng Zhang 002; Han Wang 006 (CoRR, 2022)

A deep variational free energy approach to dense hydrogen. - Hao Xie; Zi-Hang Li; Han Wang 006; Linfeng Zhang 002; Lei Wang (CoRR, 2022)

Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics. - Dongdong Wang; Yanze Wang; Junhan Chang; Linfeng Zhang 002; Han Wang 006; Weinan E (Nat. Comput. Sci., 2022)

DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models. - Yixiao Chen; Linfeng Zhang 002; Han Wang 006; Weinan E (Comput. Phys. Commun., 2023)

Dflow, a Python framework for constructing cloud-native AI-for-Science workflows. - Xinzijian Liu; Yanbo Han; Zhuoyuan Li; Jiahao Fan; Chengqian Zhang; Jinzhe Zeng; Yifan Shan; Yannan Yuan; Wei-Hong Xu; Yun-Pei Liu; Yuzhi Zhang; Tongqi Wen; et al. (CoRR, 2024)

DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials. - Jinzhe Zeng; Timothy J. Giese; Duo Zhang; Han Wang 006; Darrin M. York (J. Chem. Inf. Model., 2025)

The OpenLAM Challenges. - Anyang Peng; Xinzijian Liu; Ming-Yu Guo; Linfeng Zhang 002; Han Wang 006 (CoRR, 2025)