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{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}
}

@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},
}

@misc{Zhang_2022_DPA1,
    annote = {attention-based descriptor},
    author = {Zhang, Duo and Bi, Hangrui and Dai, Fu-Zhi and Jiang, Wanrun and Zhang, Linfeng and Wang, Han},
    title = {{DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation}},
    publisher = {arXiv},
    year = {2022},
    doi = {10.48550/arXiv.2208.08236},
}

@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}
}

@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},
}