Authors and Credits

Authors and Credits#

Cite DeePMD-kit and methods#

  • For general purpose,

[WZHE18]

Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Comm., 228:178–184, jul 2018. doi:10.1016/j.cpc.2018.03.016.

[ZZL+23]

Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, Marián Rynik, Li'ang Huang, Ziyao Li, Shaochen Shi, Yingze Wang, Haotian Ye, Ping Tuo, Jiabin Yang, Ye Ding, Yifan Li, Davide Tisi, Qiyu Zeng, Han Bao, Yu Xia, Jiameng Huang, Koki Muraoka, Yibo Wang, Junhan Chang, Fengbo Yuan, Sigbjørn Løland Bore, Chun Cai, Yinnian Lin, Bo Wang, Jiayan Xu, Jia-Xin Zhu, Chenxing Luo, Yuzhi Zhang, Rhys E A Goodall, Wenshuo Liang, Anurag Kumar Singh, Sikai Yao, Jingchao Zhang, Renata Wentzcovitch, Jiequn Han, Jie Liu, Weile Jia, Darrin M York, Weinan E, Roberto Car, Linfeng Zhang, and Han Wang. DeePMD-kit v2: A software package for deep potential models. J. Chem. Phys., 159:054801, 2023. doi:10.1063/5.0155600.

  • If GPU version is used,

[LWC+21]

Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, Weile Jia, and Linfeng Zhang. 86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy. Comput. Phys. Comm., 259:107624, 2021. doi:10.1016/j.cpc.2020.107624.

  • If local frame (loc_frame) is used,

[ZHW+18]

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett., 120(14):143001, 2018. doi:10.1103/PhysRevLett.120.143001.

  • If DeepPot-SE (se_e2_a, se_e2_r, se_e3, se_atten) is used,

[ZHW+18]

Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, and Weinan E. End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31, pages 4436–4446. Curran Associates, Inc., 2018. URL: https://dl.acm.org/doi/10.5555/3327345.3327356.

  • If three-body embedding DeepPot-SE (se_e3) is used,

[WWZ+22]

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. Nucl. Fusion, 62:126013, 2022. doi:10.1088/1741-4326/ac888b.

  • If attention-based descriptor (se_atten, se_atten_v2) is used,

[ZBD+24]

Duo Zhang, Hangrui Bi, Fu-Zhi Dai, Wanrun Jiang, Xinzijian Liu, Linfeng Zhang, and Han Wang. Pretraining of attention-based deep learning potential model for molecular simulation. Npj Comput. Mater, 10:94, 2024. doi:10.1038/s41524-024-01278-7.

  • If DPA-2 descriptor (dpa2) is used,

[ZLZ+24]

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

  • If frame-specific parameters (fparam, e.g. electronic temperature) is used,

[ZGL+20]

Yuzhi Zhang, Chang Gao, Qianrui Liu, Linfeng Zhang, Han Wang, and Mohan Chen. Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics. Phys. Plasmas, 27(12):122704, 12 2020. doi:10.1063/5.0023265.

  • If atom-specific parameters (aparam, e.g. electronic temperature) is used,

[ZCZ+23]

Qiyu Zeng, Bo Chen, Shen Zhang, Dongdong Kang, Han Wang, Xiaoxiang Yu, and Jiayu Dai. Full-scale ab initio simulations of laser-driven atomistic dynamics. 2023. doi:10.48550/arXiv.2308.13863.

  • If fitting dipole,

[ZCW+20]

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, and Roberto Car. Deep neural network for the dielectric response of insulators. Phys. Rev. B, 102(4):041121, 2020. doi:10.1103/PhysRevB.102.041121.

  • If fitting polarizability,

[SAZ+20]

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(19):10592–10602, 2020. doi:10.1039/D0CP01893G.

  • If fitting density of states,

[ZCY+22]

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. Phys. Rev. B, 105:174109, 2022. doi:10.1103/PhysRevB.105.174109.

  • If fitting relative energies,

[ZTGY23]

Jinzhe Zeng, Yujun Tao, Timothy J Giese, and Darrin M York. QD\pi : A Quantum Deep Potential Interaction Model for Drug Discovery. J. Chem. Theory Comput., 19:1261–1275, 2023. doi:10.1021/acs.jctc.2c01172.

  • If DPLR is used, or se_e2_r and hybrid are used,

[ZWM+22]

Linfeng Zhang, Han Wang, Maria Carolina Muniz, Athanassios Z Panagiotopoulos, Roberto Car, and Weinan E. A deep potential model with long-range electrostatic interactions. J. Chem. Phys., 156:124107, 2022. doi:10.1063/5.0083669.

  • If DPRc is used,

[ZGEY21]

Jinzhe Zeng, Timothy J Giese, Şölen Ekesan, and Darrin M York. Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution. J. Chem. Theory Comput., 17:6993–7009, 2021. doi:10.1021/acs.jctc.1c00201.

  • If interpolation with a pair-wise potential is used,

[WGZ+19]

Hao Wang, Xun Guo, Linfeng Zhang, Han Wang, and Jianming Xue. Deep learning inter-atomic potential model for accurate irradiation damage simulations. Appl. Phys. Lett., 114(24):244101, 2019. doi:10.1063/1.5098061.

  • If the model is compressed (dp compress),

[LJC+22]

Denghui Lu, Wanrun Jiang, Yixiao Chen, Linfeng Zhang, Weile Jia, Han Wang, and Mohan Chen. DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models. J. Chem. Theory Comput., 18:5555–5567, 2022. doi:10.1021/acs.jctc.2c00102.

  • If model deviation is computed,

[ZLW+19]

Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, and Weinan E. Active learning of uniformly accurate interatomic potentials for materials simulation. Phys. Rev. Mater., 3:23804, 2019. doi:10.1103/PhysRevMaterials.3.023804.

  • If relative or atomic model deviation is computed,

[ZZWZ21]

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. doi:10.1021/acs.energyfuels.0c03211.

  • If NVNMD is used,

[MLZ+22]

Pinghui Mo, Chang Li, Dan Zhao, Yujia Zhang, Mengchao Shi, Junhua Li, and Jie Liu. Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture. npj Comput. Mater., 8:107, 2022. doi:10.1038/s41524-022-00773-z.

Package Contributors#

  • A bot of @njzjz

  • AngelJia

  • AnguseZhang

  • Anurag Kumar Singh

  • Anyang Peng

  • Chenqqian Zhang

  • Chenxing Luo

  • Chun Cai

  • Davide Tisi

  • Denghui Lu

  • Duo

  • Eisuke Kawashima

  • Futaki Haduki

  • GeiduanLiu

  • Han Wang

  • Hananeh Oliaei

  • Harvey Que

  • HuangJiameng

  • HydrogenSulfate

  • Jia-Xin Zhu

  • Jiequn Han

  • Jingchao Zhang

  • Jinzhe Zeng

  • Koki MURAOKA

  • LiangWenshuo1118

  • Linfeng Zhang

  • LiuGroupHNU

  • Lu

  • Lysithea

  • Marián Rynik

  • Nick Lin

  • Rhys Goodall

  • Shaochen Shi

  • Sigbjørn Løland Bore

  • TrellixVulnTeam

  • Wanrun Jiang

  • Xia, Yu

  • Xu zhangmancang

  • YWolfeee

  • Yan Wang

  • Ye Ding

  • Yifan Li李一帆

  • Yingze Wang

  • Yixiao Chen

  • Yong-Bin Zhuang

  • Zeyu Li

  • ZhengdQin

  • ZiyaoLi

  • baohan

  • bwang-ecnu

  • deepmodeling

  • denghuilu

  • dependabot[bot]

  • haidi

  • hlyang

  • hsulab

  • hztttt

  • iProzd

  • iid-ccme

  • imgbot[bot]

  • jxxiaoshaoye

  • liangadam

  • likefallwind

  • link89

  • marian-code

  • mingzhong15

  • nahso

  • njzjz

  • pkulzy

  • pre-commit-ci[bot]

  • readthedocs-assistant

  • shiruosong

  • sigbjobo

  • tuoping

  • wsyxbcl

  • zhenyu

  • ziyao

Other Credits#

  • Zhang ZiXuan for designing the Deepmodeling logo.

  • Everyone on the Deepmodeling mailing list for contributing to many discussions and decisions!