You must be logged in to sponsor njzjz
Become a sponsor to Jinzhe Zeng
I am currently a PhD candidate in Chemistry at Rutgers University. My research focuses on computational chemistry. I have published the following papers that are related to scientific software in computational chemistry:
- 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.
- 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.
The source codes for these packages are held on GitHub, shown below. The sponsorship will encourage me to maintain the above scientific software long-term.
Featured work
-
deepmodeling/deepmd-kit
A deep learning package for many-body potential energy representation and molecular dynamics
C++ 1,491 -
deepmodeling/reacnetgenerator
an automatic reaction network generator for reactive molecular dynamics simulation
Python 67 -
deepmodeling/dpgen
The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field
Python 306