🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

Jinzhe Zeng

University of Science and Technology of China

ORCID: 0000-0002-1515-8172

Also affiliated with Suzhou University of Science and Technology

Materials ScienceBiochemistry, Genetics and Molecular BiologyComputer SciencePhysics and AstronomyChemical Engineering

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 6%percentile

0.298Author DataRank

Indexed papers

1in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Why this DataRank?

An author's DataRank is the sum of the DataRanks of all 1 indexed paper attributed to them. A prolific author with many moderate-impact papers can outrank one with a single high-impact paper.

Author scores recompute whenever paper DataRanks are refreshed, so this number lags the underlying paper scores by at most one batch run.

Read the full methodology →

The highest-impact dataset this researcher has shared, ranked by DataRank — the single contribution doing the most to lift their data-sharing standing.

The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions

Scientific Data(2025)10.1038/s41597-025-04972-3

Top 55%

0.298DataRank

Top 55%5 citations

Timothy J. Giese, Andreas Goetz, Darrin M. York, Jinzhe Zeng

Papers

Driven by 19 papers — median percentile 45. Top paper: “The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions”.

The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions

Scientific Data(2025)10.1038/s41597-025-04972-3

Top 55%

0.298DataRank

Top 55%5 citations

Timothy J. Giese, Andreas Goetz, Darrin M. York, Jinzhe Zeng

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

ChemRxiv(2021)10.26434/chemrxiv.14120447.v1

N/A

0DataRank · unranked

6 citations

Timothy J. Giese, Şölen Ekesan, Darrin M. York, Jinzhe Zeng

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

ChemRxiv(2021)10.26434/chemrxiv.14120447

N/A

0DataRank · unranked

3 citations

Timothy J. Giese, Şölen Ekesan, Darrin M. York, Jinzhe Zeng

Ab initio neural network MD simulation of thermal decomposition of a high energy material CL-20/TNT

Physical Chemistry Chemical Physics(2022)10.1039/d2cp00710j

N/A

0DataRank · unranked

62 citations

Jinzhe Zeng, Bo Wang, Tong Zhu, John Z. H. Zhang, Liqun Cao

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)10.1021/acs.jctc.2c00151

N/A

0DataRank · unranked

42 citations

Jinzhe Zeng, Şölen Ekesan, Darrin M. York, Timothy J. Giese

Multireference Generalization of the Weighted Thermodynamic Perturbation Method

The Journal of Physical Chemistry A(2022)10.1021/acs.jpca.2c06201

N/A

0DataRank · unranked

23 citations

Jinzhe Zeng, Darrin M. York, Timothy J. Giese

Growth of Polycyclic Aromatic Hydrocarbon and Soot Inception by in silico Simulation

ChemRxiv(2022)10.26434/chemrxiv-2022-qp8fc

N/A

0DataRank · unranked

3 citations

Jinzhe Zeng, Liqun Cao, Chih‐Hao Chin, Darrin M. York, Tong Zhu +2 more

DeePMD-kit v2: A software package for deep potential models

The Journal of Chemical Physics(2023)10.1063/5.0155600

N/A

0DataRank · unranked

552 citations

Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen +42 more

QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery

Journal of Chemical Theory and Computation(2023)10.1021/acs.jctc.2c01172

N/A

0DataRank · unranked

46 citations

Yujun Tao, Timothy J. Giese, Darrin M. York, Jinzhe Zeng

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)10.1021/acs.jctc.1c00201

N/A

0DataRank · unranked

106 citations

Timothy J. Giese, Şölen Ekesan, Darrin M. York, Jinzhe Zeng

DeePMD-kit v2: A software package for Deep Potential models

arXiv (Cornell University)(2023)10.48550/arxiv.2304.09409

N/A

0DataRank · unranked

15 citations

Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen +42 more

DPA-2: a large atomic model as a multi-task learner

arXiv (Cornell University)(2023)10.48550/arxiv.2312.15492

N/A

0DataRank · unranked

8 citations

Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi +38 more

DPA-2: a large atomic model as a multi-task learner

npj Computational Materials(2024)10.1038/s41524-024-01493-2

N/A

0DataRank · unranked

124 citations

Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi +37 more

Software Infrastructure for Next-Generation QM/MM−ΔMLP Force Fields

The Journal of Physical Chemistry B(2024)10.1021/acs.jpcb.4c01466

N/A

0DataRank · unranked

29 citations

Jinzhe Zeng, Lauren Lerew, Erika McCarthy, Yujun Tao, Şölen Ekesan +2 more

DPA-2: Towards a universal large atomic model for molecular and materials simulation

Research Square(2024)10.21203/rs.3.rs-4100052/v1

N/A

0DataRank · unranked

11 citations

Duo Zhang, Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai +37 more

Recent Developments in Amber Biomolecular Simulations

Journal of Chemical Information and Modeling(2025)10.1021/acs.jcim.5c01063

N/A

0DataRank · unranked

131 citations

David S. Cerutti, Vinícius Wilian D. Cruzeiro, Thomas A. Darden, Robert E. Duke, Mahdieh Ghazimirsaeed +26 more

DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials

Journal of Chemical Information and Modeling(2025)10.1021/acs.jcim.4c02441

N/A

0DataRank · unranked

21 citations

Timothy J. Giese, Duo Zhang, Han Wang, Darrin M. York, Jinzhe Zeng

Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications

The Journal of Physical Chemistry B(2025)10.1021/acs.jpcb.5c02006

N/A

0DataRank · unranked

11 citations

Jinzhe Zeng, Darrin M. York, Timothy J. Giese

Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states

The Journal of Chemical Physics(2023)10.1063/5.0139281

N/A

0DataRank · unranked

24 citations

Yujun Tao, Timothy J. Giese, Darrin M. York, Jinzhe Zeng