• B.S. in Chemistry, East China Normal University, Sep 2015 - Jul 2019
• PhD in Chemistry, Rutgers University, Aug 2019 - Jan 2025 (expected)

• Research Volunteer, East China Normal University, $150/month, Sep 2017 - Jul 2019
• Undergraduate Internship Course for 6 Credits, Shanghai Qiaokun Chemical Technology Co., LTD, $340/month, Sep 2018 - Nov 2018
• Summer Research Internship, Beijing Institute of Big Data Research, $1000/month, Jun 2019 - Jul 2019
• Rutgers University, Sep 2019 - Jan 2025 (expected)
  • Remained Fellow, $3000/month, Sep 2019 - Aug 2020
  • Graduate Assistant, $3000/month, Aug 2020 - June 2022
  • Student Worker, $2000/month, July 2022 - Aug 2022
  • Graduate Assistant, $3000/month, Sep 2022 - June 2023
  • Coadjutant, $3000/month, July 2023 - Aug 2023
  • Graduate Assistant, $3500/month, Sep 2023 - June 2024
  • Coadjutant, $3500/month, July 2024 - Aug 2024
  • Graduate Assistant, $3500/month, Sep 2024 - Jan 2025 (expected)
• Tenure-track Assistant Professor, University of Science and Technology of China, Mar 2025 (expected)

Develop deep learning potentials for simulations of different applications, including combustion of hydrocarbon fuels, RNA catalysis reactions, and drug discovery.

• Timothy J. Giese, Jinzhe Zeng, Lauren Lerew, Erika McCarthy, Yujun Tao, Şölen Ekesan, Darrin M. York*, Software Infrastructure for Next-Generation QM/MM−ΔMLP Force Fields, Journal of Physical Chemistry B, 2024, DOI: 10.1021/acs.jpcb.4c01466.
• Yujun Tao, Timothy J. Giese, Şölen Ekesan, Jinzhe Zeng, Bálint Aradi, Ben Hourahine, H. Metin Aktulga, Andreas Walter Götz, Kenneth M. Merz, Jr., Darrin M. York*, Amber free energy tools: Interoperable software for free energy simulations using generalized quantum mechanical/molecular mechanical and machine learning potentials, The Journal of Chemical Physics, 2024, 160, 224104, DOI: 10.1063/5.0211276.
• 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, Darrin M York, Zhicheng Zhong, Hang Zheng, Jun Cheng, Linfeng Zhang*, Han Wang*, Dflow, a Python framework for constructing cloud-native AI-for-Science workflows, arxiv:2404.18392, DOI: 10.48550/arXiv.2404.18392.
• Duo Zhang, Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi, Yiming Du, Xuejian Qin, 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*, Han Wang*, DPA-2: Towards a universal large atomic model for molecular and material simulation, arXiv:2312.15492, DOI: 10.48550/arXiv.2312.15492.
• 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, Han Wang*, DeePMD-kit v2: A software package for Deep Potential models, The Journal of Chemical Physics, 2023, 159, 054801, DOI: 10.1063/5.0155600.
• Jinzhe Zeng, Yujun Tao, Timothy J. Giese, Darrin M. York*, Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: conformers, tautomers and protonation states, The Journal of Chemical Physics, 2023, 158, 124110, DOI: 10.1063/5.0139281.
• Wenshuo Liang, Jinzhe Zeng, Darrin M. York, Linfeng Zhang, Han Wang, Learning DeePMD-kit: A guide to building deep potential models, in Yong Wang and Ruhong Zhou (Eds.), A Practical Guide to Recent Advances in Multiscale Modeling and Simulation of Biomolecules (pp. 6-1–6-20), AIP Publishing, Melville, New York, 2023, DOI: 10.1063/9780735425279_006.
• Jinzhe Zeng, Yujun Tao, Timothy J. Giese, Darrin M. York*, QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery, Journal of Chemical Theory and Computation, 2023, 19, 4, 1261-1275, DOI: 10.1021/acs.jctc.2c01172.
• Timothy J. Giese, Jinzhe Zeng, Darrin M. York*, Multireference Generalization of the Weighted Thermodynamic Perturbation Method, Journal of Physcial Chemistry A, 2022, 126, 45, 8519-8533, DOI: 10.1021/acs.jpca.2c06201.
• Jinzhe Zeng, Liqun Cao, Tong Zhu*. Chapter 12 - Neural network potentials. in Pavlo O. Dral (Eds.), Quantum Chemistry in the Age of Machine Learning (pp. 279-294), Elsevier, 2023, DOI: 10.1016/B978-0-323-90049-2.00001-9.
• Timothy J. Giese, ̧Jinzhe Zeng, Sölen Ekesan, Darrin M. York*, Combined QM/MM, Machine Learning Path Integral Approach to Compute Free Energy Profiles and Kinetic Isotope Effects in RNA Cleavage Reactions, Journal of Chemical Theory and Computation, 2022, 18 (7), 4304-4317, DOI: 10.1021/acs.jctc.2c00151.
• Liqun Cao, Jinzhe Zeng, Bo Wang, Tong Zhu*, John Z.H. Zhang*, Ab Initio Neural Network MD Simulation of Thermal Decomposition of High Energy Material CL-20/TNT, Physical Chemistry Chemical Physics, 2022, 24 (19), 11801-11811, DOI: 10.1039/D2CP00710J.
• Jinzhe Zeng, Timothy J. Giese, ̧Sölen Ekesan, Darrin M. York*, Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution, Journal of Chemical Theory and Computation, 2021, 17 (11), 6993-7009, DOI: 10.1021/acs.jctc.1c00201.
• Liqun Cao, Jinzhe Zeng, Mingyuan Xu, Chih-Hao Chin, Tong Zhu*, John ZH Zhang*, Fragment-based Ab Initio Molecular Dynamics Simulation for Combustion, Molecules, 2021, 26 (11), 3120, DOI: 10.3390/molecules26113120.
• Jinzhe Zeng, Linfeng Zhang*, Han Wang*, Tong Zhu*, Exploring the Chemical Space of Linear Alkanes Pyrolysis via Deep Potential GENerator, Energy & Fuels, 2021, 35 (1), 762-769, DOI: 10.1021/acs.energyfuels.0c03211.
• Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu*, John Z. H. Zhang*, Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation, Nature Communications, 2020, 11, 5713, DOI: 10.1038/s41467-020-19497-z.
• 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, Comput. Phys. Commun, 2020, 253, 107206, DOI: 10.1016/j.cpc.2020.107206.
• Jinzhe Zeng, Liqun Cao, Chih-Hao Chin*, Haisheng Ren, John Z. H. Zhang*, Tong Zhu*, ReacNetGenerator: an automatic reaction network generator for reactive molecular dynamics simulations, Phys. Chem. Chem. Phys., 2020, 22 (2), 683–691, DOI: 10.1039/C9CP05091D.
• Shuaizhen Tian, Jinzhe Zeng, Xiao Liu*, Jianzhong Chen, John ZH Zhang, Tong Zhu*, Understanding the selectivity of inhibitors toward PI4KIIIα and PI4KIIIβ based molecular modeling, Physical Chemistry Chemical Physics, 2019, 21 (39), 22103-22112, DOI: 10.1039/C9CP03598B.
• Xin-Yue Han, Zi-Han Chen, Jin-Zhe Zeng, Qian-Xi Fan, Zheng-Qi Fang, Guoyue Shi, Min Zhang*, Inorganic-Organic Hybrid Tongue-Mimic for Time-Resolved Luminescent Noninvasive Pattern and Chiral Recognition of Thiols in Biofluids toward Healthcare Monitoring, ACS Applied Materials & Interfaces, 2018, 10 (37), 31725-31734, DOI: 10.1021/acsami.8b13498.