1. Duo Zhang, Hangrui Bi, Fu-Zhi Dai, Wanrun Jiang, Xinzijian Liu, Linfeng Zhang & Han Wang
    Pretraining of attention-based deep learning potential model for molecular simulation
    npj Computational Materials, 10, 94 (2024).

  2. Yi-Nan Wang, Xiao-Yang Wang, Wan-Run Jiang, Han Wang and Fu-Zhi Dai
    Domain structures and stacking sequences of Mg–Zn–Y long-period stacking ordered (LPSO) structures predicted by Deep-learning Potential
    Materials Today Communications, 38, 108301 (2024).

  3. Zhenyu Wang, Xiaoyang Wang, Xiaoshan Luo, Pengyue Gao, Ying Sun, Jian Lv, Han Wang, Yanchao Wang, and Yanming Ma
    Concurrent learning scheme for crystal structure prediction
    Phys. Rev. B, 109, 094117 (2024).

  4. Jin Xiao, YiXiao Chen, LinFeng Zhang, Han Wang, Tong Zhu
    A machine learning-based high-precision density functional method for drug-like molecules
    Artificial Intelligence Chemistry, 2, 100037 (2024).

  5. Qiyu Zeng, Bo Chen, Shen Zhang, Dongdong Kang, Han Wang, Xiaoxiang Yu & Jiayu Dai
    Full-scale ab initio simulations of laser-driven atomistic dynamics
    npj Computational Materials, 9, 213 (2023).

  6. Hao Xie, Zi-Hang Li, Han Wang, Linfeng Zhang, and Lei Wang
    Deep variational free energy approach to dense hydrogen
    Phys. Rev. Letts., 131, 126501 (2023).

  7. Xiao-Yang Wang, Yi-Nan Wang, Ke Xu, Fu-Zhi Dai, Hai-Feng Liu, Guang-Hong Lu, and Han Wang
    Deep neural network potential for simulating hydrogen blistering in tungsten
    Phys. Rev. Materials, 7, 093601 (2023).

  8. Xiaoyang Wang, Zhenyu Wang, Pengyue Gao, Chengqian Zhang, Jian Lv, Han Wang, Haifeng Liu, Yanchao Wang, Yanming Ma
    Data-driven prediction of complex crystal structures of dense lithium
    Nature Communications, 14, 2924 (2023).

  9. Yixiao Chen, Linfeng Zhang, Han Wang, Weinan E
    DeePKS-kit: a package for developing machine learning-based chemically accurate energy and density functional models
    Computer Physics Communications, 282, 108520 (2023).

  10. Jinzhe Zeng
    DeePMD-kit v2: A software package for deep potential models
    J. Chem. Phys., 159, 054801 (2023).

  11. Tongqi Wen, Anwen Liu, Rui Wang, Linfeng Zhang, Jian Han, Han Wang, David J. Srolovitz, Zhaoxuan Wu
    Modelling of dislocations, twins and crack-tips in HCP and BCC Ti
    International Journal of Plasticity, 166, 103644 (2023).

  12. Di Zhao, Weiming Li, Wengu Chen, Peng Song, and Han Wang
    RNN-Attention Based Deep Learning for Solving Inverse Boundary Problems in Nonlinear Marshak Waves
    Journal of Machine Learning, in press (2023).

  13. Denghui Lu; Wanrun Jiang; Yixiao Chen; Linfeng Zhang; Weile Jia; Han Wang; Mohan Chen
    DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models
    Journal of Chemical Theory and Computation, 18, 5559-5567 (2022).

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

  15. Rui Wang, Xiaoxiao Ma, Linfeng Zhang, Han Wang, David J Srolovitz, Tongqi Wen, Zhaoxuan Wu
    Classical and machine learning interatomic potentials for BCC vanadium
    Phys. Rev. Materials, 6, 113603 (2022).

  16. Xiaoyang Wang, Yinan Wang, Linfeng Zhang, Fuzhi Dai and Han Wang
    A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment
    Nuclear Fusion, 62 126013 (2022).

  17. Qiyu Zeng, Bo Chen, Xiaoxiang Yu, Shen Zhang, Dongdong Kang, Han Wang, and Jiayu Dai
    Towards large-scale and spatiotemporally resolved diagnosis of electronic density of states by deep learning
    Physical Review B, 105, 174109 (2022).

  18. Tongqi Wen, Linfeng Zhang, Han Wang, Weinan E, and David J Srolovitz,
    Deep potentials for materials science
    Materials Futures, 1, 022601 (2022).

  19. Linfeng Zhang, Han Wang, Maria Carolina Muniz, Athanassios Z. Panagiotopoulo, Roberto Car, Weinan E
    A deep potential model with long-range electrostatic interactions
    The Journal of Chemical Physical, 156, 124107 (2022).

  20. Dongdong Wang, Yanze Wang, Junhan Chang, Linfeng Zhang, Han Wang, Weinan E
    Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics
    Nature Computational Science, 2, 20-29 (2021).

  21. Davide Tisi, Linfeng Zhang, Riccardo Bertossa, Han Wang, Roberto Car, and Stefano Baroni
    Heat transport in liquid water from first-principles and deep neural network simulations
    Physical Review B, 104, 224202 (2021).

  22. YiNan Wang, LinFeng Zhang, Ben Xu, XiaoYang Wang and Han Wang
    A generalizable machine learning potential of Ag–Au nanoalloys and its application to surface reconstruction, segregation and diffusion
    Modelling and Simulation in Materials Science and Engineering, 30 025003 (2021).

  23. Tongqi Wen, Rui Wang, Lingyu Zhu, Linfeng Zhang, Han Wang, David J. Srolovitz and Zhaoxuan Wu
    Specialising neural network potentials for accurate properties and application to the mechanical response of titanium
    npj Computational Materials volume, 7, 206 (2021).

  24. Linfeng Zhang, Han Wang, Roberto Car and Weinan E
    The Phase Diagram of a Deep Potential Water Model
    Physical Review Letters, 126(23), 236001 (2021).

  25. Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang and Han Wang
    Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space
    Chinese Physics B, 30(5), 050706 (2021).

  26. Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, WeileJia, Linfeng Zhang
    86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
    Computer Physics Communications, 259, 107624 (2021).

  27. Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng and Weinan E
    Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
    The Journal of Chemical Physics,154, 094703 (2021).

  28. Nanyun Bao, Fangyu Guo, Dongdong Kang, Yexin Feng, Han Wang, and Jiayu Dai
    Toward accurate electronic, optical, and vibrational properties of hexagonal Si, Ge, and Si1−xGex alloys from first-principle simulations
    Journal of Applied Physics,129, 145701 (2021).

  29. Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Lin Lin, Roberto Car, Weinan E and Linfeng Zhang
    Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
    SC'20: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 5 1-14 (2020).

  30. Jinzhe Zeng, Linfeng Zhang*, Han Wang*, and Tong Zhu*
    Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator
    Energy Fuels, 35(1) 762–769 (2021).

  31. Yixiao Chen, Linfeng Zhang*, Han Wang*, and Weinan E
    DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory
    Journal of Chemical Theory and Computation, 17(1) 170–181 (2021).

  32. Yixiao Chen, Linfeng Zhang, Han Wang, and Weinan E
    Ground State Energy Functional with Hartree-Fock Efficiency and Chemical Accuracy
    J. Phys. Chem. A, 124(35), 7155–7165 (2020).

  33. Haidi Wang, Yuzhi Zhang, Linfeng Zhang* and Han Wang*
    Crystal Structure Prediction of Binary Alloys via Deep Potential
    Front. Chem., 8, 589795 (2020).

  34. Grace M. Sommers, Marcos F. Calegari Andrade, Linfeng Zhang, Han Wang and Roberto Car
    Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks
    Phys. Chem. Chem. Phys., 22, 10592-10602 (2020).

  35. Yuzhi Zhang, Chang Gao, Qianrui Liu, Linfeng Zhang, Han Wang, Mohan Chen
    Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics
    Physics of Plasmas, 27, 122704 (2020).

  36. Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, and Roberto Car
    Deep neural network for the dielectric response of insulators
    Physical Review B, 102, 041121(R) (2020).

  37. Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang*, Han Wang*, Weinan E*
    DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
    Computer Physics Communications, 253, 107206 (2020).

  38. Tian Tian, Han Wang, Wei Ge, Pingwen Zhang*
    Detecting Particle Clusters in Particle-Fluid Systems by a Density Based Method
    Communications in Computational Physics, 26(5): 1617-1630 (2019).

  39. Hsin-Yu Ko, Linfeng Zhang, Biswajit Santra, Han Wang, Weinan E, Robert A. DiStasio Jr., Roberto Car*
    Isotope Effects in Liquid Water via Deep Potential Molecular Dynamics
    Molecular Physics, 117(22), 3269-3281 (2019).

  40. Hao Wang, Xun Guo, Linfeng Zhang, Han Wang*, and Jianming Xue*
    Deep learning inter-atomic potential model for accurate irradiation damage simulations
    Applied Physics Letters, 114, 244101 (2019).

  41. Linfeng Zhang, De-Ye Lin, Han Wang*, Roberto Car and Weinan E*
    Active learning of uniformly accurate interatomic potentials for materials simulation
    Physical Review Materials, 3, 023804 (2019).

  42. Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, Weinan E
    End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
    Advances in Neural Information Processing Systems, 31, 4436-4446 (2018).

  43. Linfeng Zhang, Han Wang* and Weinan E*
    Adaptive coupling of a deep neural network potential to a classical force field
    The Journal of Chemical Physics, 149, 154107 (2018).

  44. Linfeng Zhang, Jiequn Han, Han Wang*, Roberto Car, and Weinan E*
    DeePCG: Constructing coarse-grained models via deep neural networks
    The Journal of Chemical Physics, 149, 034101 (2018).

  45. Linfeng Zhang, Han Wang*, Weinan E*,
    Reinforced dynamics for enhanced sampling in large atomic and molecular systems
    The Journal of Chemical Physics, 148, 124113 (2018).

  46. Han Wang*, Linfeng Zhang*, Jiequn Han, Weinan E,
    DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
    Computer Physics Communications, 228, 178-184, (2018).

  47. Linfeng Zhang, Jiequn Han, Han Wang*, Roberto Car, Weinan E*,
    Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
    Physical Review Letters, 120, 143001 (2018).

  48. Yuzhi Zhou, Han Wang, Yu Liu, Xingyu Gao*, Haifeng Song,
    Applicability of Kerker preconditioning scheme to the self-consistent density functional theory calculations of inhomogeneous systems
    Physical Review E, 97, 033305 (2018).

  49. Han Wang*, Jun Fang, Xingyu Gao,
    The optimal particle-mesh interpolation basis
    The Journal of Chemical Physics, 147, 124107 (2017).

  50. Xingyu Gao, Jun Fang, Han Wang*,
    Kaiser-Bessel basis for particle-mesh interpolation
    Physical Review E, 95, 063303 (2017).

  51. GuoMin Han, Han Wang, De-Ye Lin, XueYan Zhu, ShenYang Hu*, HaiFeng Song*,
    Phase-field modeling of void anisotropic growth behavior in irradiated zirconium
    Computational Materials Science, 133, 22-34 (2017).

  52. Xingyu Gao, Zeyao Mo, Jun Fang, Haifeng Song, Han Wang*,
    Parallel 3-dim fast Fourier transforms with load balancing of the plane waves
    Computer Physics Communications, 211, 54-60 (2017).

  53. Han Wang*, Xingyu Gao and Jun Fang
    Multiple Staggered Mesh Ewald: Boosting the Accuracy of the Smooth Particle Mesh Ewald Method
    Journal of Chemical Theory and Computation, 12(11), 5596-5608 (2016).

  54. Jun Fang, Gao Xingyu, Haifeng Song, Han Wang*,
    On the existence of the optimal order for wavefunction extrapolation in Born-Oppenheimer molecular dynamics
    The Journal of Chemical Physics, 144, 244103 (2016).

  55. Xingyu Gao, Jun Fang, Han Wang*,
    Sampling the isothermal-isobaric ensemble by Langevin dynamics
    The Journal of Chemical Physics, 144, 124113 (2016).

  56. Han Wang*, Haruki Nakamura, Ikuo Fukuda*
    A Critical Appraisal of the Zero-Multipole Method: Structural, Thermodynamic, Dielectric, and Dynamical Properties of a Water System
    The Journal of Chemical Physics, 144, 114503 (2016).

  57. Shuyu Chen, Han Wang, Tiezheng Qian and Ping Sheng*
    Determining hydrodynamic boundary conditions from equilibrium fluctuations
    Physical Review E, 92, 043007 (2015).

  58. Animesh Agarwal, Jinglong Zhu, Carsten Hartmann, Han Wang and Luigi Delle Site*
    Molecular dynamics in a grand ensemble: Bergmann-Lebowitz model and adaptive resolution simulation
    New Journal of Physics, 17(8), 083042 (2015).

  59. Han Wang* and Animesh Agarwal
    Adaptive resolution simulation in equilibrium and beyond
    The European Physical Journal Special Topics, 224, 2269--2287, (2015).

  60. Han Wang* and Christof Schütte*
    Building Markov State Models for Periodically Driven Non-Equilibrium Systems
    Journal of Chemical Theory and Computing, 11(4) 1819-1831 (2015).

  61. Wei Zhang, Han Wang, Carsten Hartmann, Markus Weber and Christof Schütte*
    Applications of the cross-entropy method to importance sampling and optimal control of diffusions
    SIAM Journal on Scientific Computing, 36(6), A2654-A2672 (2014).

  62. Han Wang*
    Error estimates for calculating the non-bonded interactions in molecular dynamics simulations (in Chinese)
    Scientia Sinica Mathematica, 44, 823-836 (2014).

  63. Animesh Agarwal, Han Wang*, Christof Schütte, and Luigi Delle Site*
    Chemical potential of liquids and mixtures via Adaptive Resolution Simulation
    The Journal of Chemical Physics, 141, 034102 (2014).

  64. Han Wang*, Christof Schütte, Giovanni Ciccotti and Luigi Delle Site
    Exploring the conformational dynamics of alanine dipeptide in solution subjected to an external electric field: A nonequilibrium molecular dynamics simulation.
    Journal of Chemical Theory and Computation, 10(4), 1376–1386 (2014).

  65. Jinglong Zhu, Pingwen Zhang, Han Wang* and Luigi Delle Site*
    Is there a third order phase transition for supercritical fluids?
    The Journal of Chemical Physics, 140, 014502 (2014).

  66. Han Wang, Carsten Hartmann and Christof Schütte*
    Linear response theory and optimal control for a molecular system under non-equilibrium conditions
    Molecular Physics, 111(22-23), 3555-3564 (2013).

  67. Han Wang, Carsten Hartmann, Christof Schütte, and Luigi Delle Site*
    Grand-canonical-like molecular-dynamics simulations by using an adaptive-resolution technique
    Physical Review X, 3, 011018 (2013).

  68. Han Wang*, Pingwen Zhang and Christof Schütte,
    On the Numerical Accuracy of Ewald, Smooth Particle Mesh Ewald, and Staggered Mesh Ewald Methods for Correlated Molecular Systems
    Journal of Chemical Theory and Computation, 8(9), 3243-3256 (2012).

  69. Han Wang*, Christof Schütte and Pingwen Zhang,
    Error estimate of short-range force calculation in inhomogeneous molecular systems.
    Physical Review E, 86(2), 026704 (2012).

  70. Han Wang, Christof Schütte and Luigi Delle Site*,
    Adaptive Resolution Simulation (AdResS): A smooth thermodynamic and structural transition from atomistic to coarse grained resolution and vice versa in a Grand Canonical fashion
    Journal of Chemical Theory and Computation, 8(8), 2878-2887 (2012).

  71. Han Wang*, Dan Hu and Pingwen Zhang,
    Measuring the spontaneous curvature of bilayer membranes by molecular dynamics simulations.
    Communications in Computational Physics, 13(4), 1093-1106 (2013).

  72. Han Wang, Luigi Delle Site and Pingwen Zhang*,
    On the existence of a third-order phase transition beyond the Andrews critical point: A molecular dynamics study.
    The Journal of Chemical Physics, 135, 224506 (2011).

  73. Han Wang*, Florian Dommert and Christian Holm,
    Optimizing working parameters of the smooth particle mesh Ewald algorithm in terms of accuracy and efficiency.
    The Journal of Chemical Physics, 133, 034117 (2010).

  74. Han Wang, Christoph Junghans* and Kurt Kremer,
    Comparative atomistic and coarse-grained study of water: What do we lose by coarse-graining?
    The European Physical Journal E: Soft Matter and Biological Physics, 28, 2, 221-229 (2009).

  75. Han Wang, Kun Li and Pingwen Zhang*,
    Crucial properties of the moment closure model FENE-QE.
    Journal of Non-Newtonian Fluid Mechanics, 150, 2-3, 80-92 (2008).

  76. Han Wang and Huazhong Tang*,
    An efficient adaptive mesh redistribution method for a non-linear Dirac equation.
    Journal of Computational Physics, 222, 1, 176-193 (2007).