CITATIONS.bib

The proposed feature of each article is described in the "annote" field.
Please cite a article if any feature is used
@article{Wang_ComputPhysCommun_2018_v228_p178,
  annote       = {general purpose},
  author       = {Wang, Han and Zhang, Linfeng and Han, Jiequn and E, Weinan},
  doi          = {10.1016/j.cpc.2018.03.016},
  year         = 2018,
  month        = {jul},
  publisher    = {Elsevier {BV}},
  volume       = 228,
  journal      = {Comput. Phys. Comm.},
  title        = {
    {DeePMD-kit: A deep learning package for many-body potential energy
    representation and molecular dynamics}
  },
  pages        = {178--184},
}

@article{Zeng_JChemPhys_2023_v159_p054801,
  annote       = {general purpose},
  title        = {{DeePMD-kit v2: A software package for deep potential models}},
  author       = {
    Jinzhe Zeng and Duo Zhang and Denghui Lu and Pinghui Mo and Zeyu Li and
    Yixiao Chen and Mari{\'a}n Rynik and Li'ang Huang and Ziyao Li and Shaochen
    Shi and Yingze Wang and Haotian Ye and Ping Tuo and Jiabin Yang and Ye Ding
    and Yifan Li and Davide Tisi and Qiyu Zeng and Han Bao and Yu Xia and
    Jiameng Huang and Koki Muraoka and Yibo Wang and Junhan Chang and Fengbo
    Yuan and Sigbj{\o}rn L{\o}land Bore and Chun Cai and Yinnian Lin and Bo
    Wang and Jiayan Xu and Jia-Xin Zhu and Chenxing Luo and Yuzhi Zhang and
    Rhys E A Goodall and Wenshuo Liang and Anurag Kumar Singh and Sikai Yao and
    Jingchao Zhang and Renata Wentzcovitch and Jiequn Han and Jie Liu and Weile
    Jia and Darrin M York and Weinan E and Roberto Car and Linfeng Zhang and
    Han Wang
  },
  journal      = {J. Chem. Phys.},
  volume       = 159,
  issue        = 5,
  year         = 2023,
  pages        = 054801,
  doi          = {10.1063/5.0155600},
}

@article{Zeng_JChemTheoryComput_2025_v21_p4375,
  annote       = {general purpose},
  author       = {
    Jinzhe Zeng and Duo Zhang and Anyang Peng and Xiangyu Zhang and Sensen He
    and Yan Wang and Xinzijian Liu and Hangrui Bi and Yifan Li and Chun Cai and
    Chengqian Zhang and Yiming Du and Jia-Xin Zhu and Pinghui Mo and Zhengtao
    Huang and Qiyu Zeng and Shaochen Shi and Xuejian Qin and Zhaoxi Yu and
    Chenxing Luo and Ye Ding and Yun-Pei Liu and Ruosong Shi and Zhenyu Wang
    and Sigbj{\o}rn L{\o}land Bore and Junhan Chang and Zhe Deng and Zhaohan
    Ding and Siyuan Han and Wanrun Jiang and Guolin Ke and Zhaoqing Liu and
    Denghui Lu and Koki Muraoka and Hananeh Oliaei and Anurag Kumar Singh and
    Haohui Que and Weihong Xu and Zhangmancang Xu and Yong-Bin Zhuang and Jiayu
    Dai and Timothy J. Giese and Weile Jia and Ben Xu and Darrin M. York and
    Linfeng Zhang and Han Wang
  },
  title        = {
    {DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning
    Potentials}
  },
  journal      = {J. Chem. Theory Comput.},
  year         = 2025,
  volume       = 21,
  number       = 9,
  pages        = {4375--4385},
  doi          = {10.1021/acs.jctc.5c00340},
  abstract     = {
    In recent years, machine learning potentials (MLPs) have become
    indispensable tools in physics, chemistry, and materials science, driving
    the development of software packages for molecular dynamics (MD)
    simulations and related applications. These packages, typically built on
    specific machine learning frameworks, such as TensorFlow, PyTorch, or JAX,
    face integration challenges when advanced applications demand communication
    across different frameworks. The previous TensorFlow-based implementation
    of the DeePMD-kit exemplified these limitations. In this work, we introduce
    DeePMD-kit version 3, a significant update featuring a multibackend
    framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle
    backends, and demonstrate the versatility of this architecture through the
    integration of other MLP packages and of differentiable molecular force
    fields. This architecture allows seamless back-end switching with minimal
    modifications, enabling users and developers to integrate DeePMD-kit with
    other packages using different machine learning frameworks. This innovation
    facilitates the development of more complex and interoperable workflows,
    paving the way for broader applications of MLPs in scientific research.
  },
}

@article{Han_CommunComputPhys_2018_v23_p629,
  annote       = {Deep Potential},
  title        = {
    {Deep Potential: A General Representation of a Many-Body Potential Energy
    Surface}
  },
  author       = {Jiequn Han and Linfeng Zhang and Roberto Car and Weinan E},
  journal      = {Commun. Comput. Phys.},
  year         = 2018,
  volume       = 23,
  number       = 3,
  pages        = {629--639},
  doi          = {10.4208/cicp.OA-2017-0213},
  url          = {https://arxiv.org/abs/1707.01478},
}

@article{Lu_CompPhysCommun_2021_v259_p107624,
  annote       = {GPU support},
  title        = {
    {86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million
    atoms with ab initio accuracy}
  },
  author       = {
    Lu, Denghui and Wang, Han and Chen, Mohan and Lin, Lin and Car, Roberto and
    E, Weinan and Jia, Weile and Zhang, Linfeng
  },
  journal      = {Comput. Phys. Comm.},
  volume       = 259,
  pages        = 107624,
  year         = 2021,
  publisher    = {Elsevier},
  doi          = {10.1016/j.cpc.2020.107624},
}

@article{Zhang_PhysRevLett_2018_v120_p143001,
  annote       = {local frame (loc\_frame)},
  author       = {Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E},
  journal      = {Phys. Rev. Lett.},
  number       = 14,
  pages        = 143001,
  publisher    = {APS},
  title        = {
    {Deep potential molecular dynamics: a scalable model with the accuracy of
    quantum mechanics}
  },
  volume       = 120,
  year         = 2018,
  doi          = {10.1103/PhysRevLett.120.143001},
}

@article{Zhang_JChemPhys_2018_v149_p34101,
  annote       = {coarse-grained model (DeePCG)},
  title        = {{DeePCG: Constructing Coarse-Grained Models via Deep Neural Networks}},
  author       = {Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E},
  journal      = {J. Chem. Phys.},
  year         = 2018,
  volume       = 149,
  number       = 3,
  pages        = 034101,
  doi          = {10.1063/1.5027645},
}

@incollection{Zhang_BookChap_NIPS_2018_v31_p4436,
  annote       = {DeepPot-SE (se\_e2\_a, se\_e2\_r, se\_e3, se\_atten)},
  title        = {
    {End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for
    Finite and Extended Systems}
  },
  author       = {
    Zhang, Linfeng and Han, Jiequn and Wang, Han and Saidi, Wissam and Car,
    Roberto and E, Weinan
  },
  booktitle    = {Advances in Neural Information Processing Systems 31},
  editor       = {
    S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N.
    Cesa-Bianchi and R. Garnett
  },
  pages        = {4436--4446},
  year         = 2018,
  publisher    = {Curran Associates, Inc.},
  url          = {https://dl.acm.org/doi/10.5555/3327345.3327356},
}

@article{Wang_NuclFusion_2022_v62_p126013,
  annote       = {three-body embedding DeepPot-SE (se\_e3)},
  author       = {Xiaoyang Wang and Yinan Wang and Linfeng Zhang and Fuzhi Dai and Han Wang},
  title        = {
    {A tungsten deep neural-network potential for simulating mechanical
    property degradation under fusion service environment}
  },
  journal      = {Nucl. Fusion},
  year         = 2022,
  volume       = 62,
  issue        = 12,
  pages        = 126013,
  doi          = {10.1088/1741-4326/ac888b},
}

@article{Zhang_NpjComputMater_2024_v10_p94,
  annote       = {DPA-1, attention-based descriptor},
  author       = {
    Duo Zhang and Hangrui Bi and Fu-Zhi Dai and Wanrun Jiang and Xinzijian Liu
    and Linfeng Zhang and Han Wang
  },
  title        = {
    {Pretraining of attention-based deep learning potential model for molecular
    simulation}
  },
  journal      = {Npj Comput. Mater},
  year         = 2024,
  volume       = 10,
  issue        = 1,
  pages        = 94,
  doi          = {10.1038/s41524-024-01278-7},
}

@article{Zhang_npjComputMater_2024_v10_p293,
  annote       = {DPA-2},
  author       = {
    Duo Zhang and Xinzijian Liu and Xiangyu Zhang and Chengqian Zhang and Chun
    Cai and Hangrui Bi and Yiming Du and Xuejian Qin and Anyang Peng and
    Jiameng Huang and Bowen Li and Yifan Shan and Jinzhe Zeng and Yuzhi Zhang
    and Siyuan Liu and Yifan Li and Junhan Chang and Xinyan Wang and Shuo Zhou
    and Jianchuan Liu and Xiaoshan Luo and Zhenyu Wang and Wanrun Jiang and
    Jing Wu and Yudi Yang and Jiyuan Yang and Manyi Yang and Fu-Qiang Gong and
    Linshuang Zhang and Mengchao Shi and Fu-Zhi Dai and Darrin M. York and Shi
    Liu and Tong Zhu and Zhicheng Zhong and Jian Lv and Jun Cheng and Weile Jia
    and Mohan Chen and Guolin Ke and Weinan E and Linfeng Zhang and Han Wang
  },
  title        = {{DPA-2: a large atomic model as a multi-task learner}},
  journal      = {npj Comput. Mater},
  year         = 2024,
  volume       = 10,
  number       = 1,
  pages        = 293,
  doi          = {10.1038/s41524-024-01493-2},
}

@article{Zhang_npjComputMater_2026_DPA3,
  annote       = {DPA-3 descriptor (dpa3)},
  author       = {
    Duo Zhang and Anyang Peng and Chun Cai and Wentao Li and Yuanchang Zhou and
    Jinzhe Zeng and Mingyu Guo and Chengqian Zhang and Bowen Li and Hong Jiang
    and Tong Zhu and Weile Jia and Linfeng Zhang and Han Wang
  },
  title        = {{A Graph Neural Network for the Era of Large Atomistic Models}},
  journal      = {npj Comput. Mater.},
  year         = 2026,
  doi          = {10.1038/s41524-026-02146-2},
}

@misc{Li_2026_DPA4,
  annote       = {DPA-4 descriptor (dpa4, sezm)},
  author       = {
    Tiancheng Li and Wentao Li and Anyang Peng and Jianming Xue and Linfeng
    Zhang and Duo Zhang and Han Wang
  },
  title        = {
    {{DPA4}: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with
    {EMFA} {SO(2)} Convolution}
  },
  publisher    = {arXiv},
  year         = 2026,
  doi          = {10.48550/arXiv.2606.02419},
  url          = {https://arxiv.org/abs/2606.02419},
}

@article{Zhang_PhysPlasmas_2020_v27_p122704,
  annote       = {frame-specific parameters (e.g. electronic temperature)},
  author       = {
    Zhang, Yuzhi and Gao, Chang and Liu, Qianrui and Zhang, Linfeng and Wang,
    Han and Chen, Mohan
  },
  title        = {
    {Warm dense matter simulation via electron temperature dependent deep
    potential molecular dynamics}
  },
  journal      = {Phys. Plasmas},
  volume       = 27,
  number       = 12,
  pages        = 122704,
  year         = 2020,
  month        = 12,
  doi          = {10.1063/5.0023265},
}

@misc{Zeng_2023_TTMDPMD,
  annote       = {atom-specific parameter (e.g. electron temperature)},
  author       = {
    Zeng, Qiyu and Chen, Bo and Zhang, Shen and Kang, Dongdong and Wang, Han
    and Yu, Xiaoxiang and Dai, Jiayu
  },
  title        = {{Full-scale ab initio simulations of laser-driven atomistic dynamics}},
  publisher    = {arXiv},
  year         = 2023,
  doi          = {10.48550/arXiv.2308.13863},
}

@article{Zhang_PhysRevB_2020_v102_p41121,
  annote       = {fit dipole},
  title        = {{Deep neural network for the dielectric response of insulators}},
  author       = {
    Zhang, Linfeng and Chen, Mohan and Wu, Xifan and Wang, Han and E, Weinan
    and Car, Roberto
  },
  journal      = {Phys. Rev. B},
  volume       = 102,
  number       = 4,
  pages        = {041121},
  year         = 2020,
  publisher    = {APS},
  doi          = {10.1103/PhysRevB.102.041121},
}

@article{Sommers_PhysChemChemPhys_2020_v22_p10592,
  annote       = {fit polarizability},
  title        = {
    {Raman spectrum and polarizability of liquid water from deep neural
    networks}
  },
  author       = {
    Sommers, Grace M and Andrade, Marcos F Calegari and Zhang, Linfeng and
    Wang, Han and Car, Roberto
  },
  journal      = {Phys. Chem. Chem. Phys.},
  volume       = 22,
  number       = 19,
  pages        = {10592--10602},
  year         = 2020,
  publisher    = {Royal Society of Chemistry},
  doi          = {10.1039/D0CP01893G},
}

@misc{Ahart_2026_DeepPolaron,
  annote       = {atomic charge population (DeepPopulation)},
  author       = {Christian S. Ahart and Denan Li and Jochen Blumberger and Shi Liu},
  title        = {
    {Polaron Transport in {TiO}$_{2}$ from Machine Learning Molecular Dynamics}
  },
  publisher    = {arXiv},
  year         = 2026,
  doi          = {10.48550/arXiv.2606.01763},
  url          = {https://arxiv.org/abs/2606.01763},
}

@article{Zeng_JChemTheoryComput_2023_v19_p1261,
  annote       = {fit relative energies},
  author       = {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York},
  title        = {{QD{\pi}: A Quantum Deep Potential Interaction Model for Drug Discovery}},
  journal      = {J. Chem. Theory Comput.},
  year         = 2023,
  volume       = 19,
  issue        = 4,
  pages        = {1261--1275},
  doi          = {10.1021/acs.jctc.2c01172},
}

@article{Zeng_PhysRevB_2022_v105_p174109,
  annote       = {fit density of states},
  author       = {
    Qiyu Zeng and Bo Chen and Xiaoxiang Yu and Shen Zhang and Dongdong Kang and
    Han Wang and Jiayu Dai
  },
  title        = {
    {Towards large-scale and spatiotemporally resolved diagnosis of electronic
    density of states by deep learning}
  },
  journal      = {Phys. Rev. B},
  year         = 2022,
  volume       = 105,
  issue        = 17,
  pages        = 174109,
  doi          = {10.1103/PhysRevB.105.174109},
}

@article{Zhang_JChemPhys_2022_v156_p124107,
  annote       = {DPLR, se\_e2\_r, hybrid descriptor},
  author       = {
    Linfeng Zhang and Han Wang and Maria Carolina Muniz and Athanassios Z
    Panagiotopoulos and Roberto Car and Weinan E
  },
  title        = {{A deep potential model with long-range electrostatic interactions}},
  journal      = {J. Chem. Phys.},
  year         = 2022,
  volume       = 156,
  issue        = 12,
  pages        = 124107,
  doi          = {10.1063/5.0083669},
}

@article{Zeng_JChemTheoryComput_2021_v17_p6993,
  annote       = {DPRc},
  title        = {
    {Development of Range-Corrected Deep Learning Potentials for Fast, Accurate
    Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions
    in Solution}
  },
  author       = {
    Zeng, Jinzhe and Giese, Timothy J and Ekesan, {\c{S}}{\"o}len and York,
    Darrin M
  },
  journal      = {J. Chem. Theory Comput.},
  year         = 2021,
  volume       = 17,
  issue        = 11,
  pages        = {6993--7009},
  doi          = {10.1021/acs.jctc.1c00201},
}

@article{Wang_ApplPhysLett_2019_v114_p244101,
  annote       = {Interpolation with a pair-wise potential},
  title        = {
    {Deep learning inter-atomic potential model for accurate irradiation damage
    simulations}
  },
  author       = {Wang, Hao and Guo, Xun and Zhang, Linfeng and Wang, Han and Xue, Jianming},
  journal      = {Appl. Phys. Lett.},
  volume       = 114,
  number       = 24,
  pages        = 244101,
  year         = 2019,
  publisher    = {AIP Publishing LLC},
  doi          = {10.1063/1.5098061},
}

@article{Zhang_PhysRevMater_2019_v3_p23804,
  annote       = {model deviation},
  title        = {
    {Active learning of uniformly accurate interatomic potentials for materials
    simulation}
  },
  author       = {Linfeng Zhang and De-Ye Lin and Han Wang and Roberto Car and Weinan E},
  journal      = {Phys. Rev. Mater.},
  volume       = 3,
  issue        = 2,
  pages        = 23804,
  year         = 2019,
  publisher    = {American Physical Society},
  doi          = {10.1103/PhysRevMaterials.3.023804},
}

@article{Lu_JChemTheoryComput_2022_v18_p5555,
  annote       = {DP Compress},
  author       = {
    Denghui Lu and Wanrun Jiang and Yixiao Chen and Linfeng Zhang and Weile Jia
    and Han Wang and Mohan Chen
  },
  title        = {
    {DP Compress: A Model Compression Scheme for Generating Efficient Deep
    Potential Models}
  },
  journal      = {J. Chem. Theory Comput.},
  year         = 2022,
  volume       = 18,
  issue        = 9,
  pages        = {5555--5567},
  doi          = {10.1021/acs.jctc.2c00102},
}

@article{Mo_npjComputMater_2022_v8_p107,
  annote       = {NVNMD},
  author       = {
    Pinghui Mo and Chang Li and Dan Zhao and Yujia Zhang and Mengchao Shi and
    Junhua Li and Jie Liu
  },
  title        = {
    {Accurate and efficient molecular dynamics based on machine learning and
    non von Neumann architecture}
  },
  journal      = {npj Comput. Mater.},
  year         = 2022,
  volume       = 8,
  issue        = 1,
  pages        = 107,
  doi          = {10.1038/s41524-022-00773-z},
}

@article{Zeng_EnergyFuels_2021_v35_p762,
  annote       = {relative or atomic model deviation},
  author       = {Jinzhe Zeng and Linfeng Zhang and Han Wang and Tong Zhu},
  title        = {
    {Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential
    GENerator}
  },
  journal      = {Energy \& Fuels},
  volume       = 35,
  number       = 1,
  pages        = {762--769},
  year         = 2021,
  doi          = {10.1021/acs.energyfuels.0c03211},
}

@article{Zhang_2026_multitaskfinetuning,
  annote       = {fit properties and multi-task fine-tuning},
  author       = {
    Chengqian Zhang and Duo Zhang and Anyang Peng and Mingyu Guo and Yuzhi
    Zhang and Lei Wang and Guolin Ke and Linfeng Zhang and Tiejun Li and Han
    Wang
  },
  title        = {
    {Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization
    in Atomistic Models}
  },
  journal      = {arXiv},
  year         = 2026,
  pages        = {2601.08486},
  doi          = {10.48550/arXiv.2601.08486},
}