Jingzhi Pu

12.1k total citations
55 papers, 2.5k citations indexed

About

Jingzhi Pu is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Materials Chemistry. According to data from OpenAlex, Jingzhi Pu has authored 55 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 24 papers in Atomic and Molecular Physics, and Optics and 16 papers in Materials Chemistry. Recurrent topics in Jingzhi Pu's work include Advanced Chemical Physics Studies (20 papers), Protein Structure and Dynamics (17 papers) and Spectroscopy and Quantum Chemical Studies (13 papers). Jingzhi Pu is often cited by papers focused on Advanced Chemical Physics Studies (20 papers), Protein Structure and Dynamics (17 papers) and Spectroscopy and Quantum Chemical Studies (13 papers). Jingzhi Pu collaborates with scholars based in United States, China and France. Jingzhi Pu's co-authors include Donald G. Truhlar, Jiali Gao, Shuhua Ma, Kwangho Nam, Yan Zhao, Martin Karplus, Benjamin J. Lynch, Dan Thomas Major, Yihan Shao and Yan Zhou and has published in prestigious journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Jingzhi Pu

55 papers receiving 2.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jingzhi Pu United States 27 1.1k 1.0k 646 446 358 55 2.5k
Daniel S. Lambrecht United States 25 1.4k 1.3× 628 0.6× 753 1.2× 406 0.9× 488 1.4× 41 2.7k
Michael Gaus Germany 17 1.1k 1.0× 836 0.8× 1.1k 1.6× 348 0.8× 413 1.2× 23 2.9k
Bella L. Grigorenko Russia 31 866 0.8× 1.7k 1.6× 759 1.2× 230 0.5× 298 0.8× 148 3.0k
Zhong‐Zhi Yang China 31 1.5k 1.4× 732 0.7× 491 0.8× 773 1.7× 417 1.2× 179 2.8k
Luis Rodríguez‐Santiago Spain 31 850 0.8× 810 0.8× 484 0.7× 901 2.0× 605 1.7× 83 2.8k
Igor A. Topol United States 32 894 0.8× 1.3k 1.2× 558 0.9× 748 1.7× 372 1.0× 109 2.9k
Mireia Garcia‐Viloca Spain 27 789 0.7× 1.8k 1.7× 844 1.3× 505 1.1× 431 1.2× 49 3.0k
Maurizio Sironi Italy 27 870 0.8× 492 0.5× 403 0.6× 452 1.0× 349 1.0× 113 2.0k
Dirk Bakowies Switzerland 25 1.2k 1.2× 1.6k 1.5× 1.1k 1.7× 1.0k 2.3× 394 1.1× 41 3.5k
Jorge Garza Mexico 29 1.3k 1.2× 385 0.4× 664 1.0× 705 1.6× 483 1.3× 127 2.9k

Countries citing papers authored by Jingzhi Pu

Since Specialization
Citations

This map shows the geographic impact of Jingzhi Pu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jingzhi Pu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingzhi Pu more than expected).

Fields of papers citing papers by Jingzhi Pu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jingzhi Pu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jingzhi Pu. The network helps show where Jingzhi Pu may publish in the future.

Co-authorship network of co-authors of Jingzhi Pu

This figure shows the co-authorship network connecting the top 25 collaborators of Jingzhi Pu. A scholar is included among the top collaborators of Jingzhi Pu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jingzhi Pu. Jingzhi Pu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Davis, G. A., et al.. (2025). Anisotropically Shaped Plasmonic WO3–x Nanostructure-Driven Ultrasensitive SERS Detection and Machine Learning-Based Differentiation of Nitro-Explosives. ACS Applied Materials & Interfaces. 17(7). 11309–11324. 12 indexed citations
2.
Van, Richard, Xiaoliang Pan, Ji Hwan Park, et al.. (2023). Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations. RSC Advances. 13(7). 4565–4577. 23 indexed citations
3.
Pan, Xiaoliang, Richard Van, Yuezhi Mao, et al.. (2023). Training machine learning potentials for reactive systems: A Colab tutorial on basic models. Journal of Computational Chemistry. 45(10). 638–647. 4 indexed citations
4.
Pan, Xiaoliang, et al.. (2023). Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation. The Journal of Chemical Physics. 159(5). 14 indexed citations
5.
Yang, Xuehui, Prashant Gupta, Barry B. Muhoberac, et al.. (2023). Hybrid Metal–Ligand Interfacial Dipole Engineering of Functional Plasmonic Nanostructures for Extraordinary Responses of Optoelectronic Properties. ACS Nano. 17(17). 17499–17515. 6 indexed citations
6.
Pan, Xiaoliang, Richard Van, Jingzhi Pu, et al.. (2023). Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions. Journal of Chemical Theory and Computation. 19(22). 8234–8244. 7 indexed citations
7.
Young, Ryan M., et al.. (2022). Photoinduced Site-Selective Functionalization of Aliphatic C–H Bonds by Pyridine N -oxide Based HAT Catalysts. ACS Catalysis. 12(16). 10441–10448. 54 indexed citations
8.
Pan, Xiaoliang, Richard Van, Evgeny Epifanovsky, et al.. (2022). Accelerating Ab Initio Quantum Mechanical and Molecular Mechanical (QM/MM) Molecular Dynamics Simulations with Multiple Time Step Integration and a Recalibrated Semiempirical QM/MM Hamiltonian. The Journal of Physical Chemistry B. 126(23). 4226–4235. 13 indexed citations
9.
Pan, Xiaoliang, et al.. (2022). Facilitating ab initio QM/MM free energy simulations by Gaussian process regression with derivative observations. Physical Chemistry Chemical Physics. 24(41). 25134–25143. 11 indexed citations
10.
Pu, Jingzhi, et al.. (2021). Interligand communication in a metal mediated LL′CT system – a case study. RSC Advances. 11(39). 24381–24386. 1 indexed citations
11.
Zhou, Yan, et al.. (2021). Reaction Path-Force Matching in Collective Variables: Determining Ab Initio QM/MM Free Energy Profiles by Fitting Mean Force. Journal of Chemical Theory and Computation. 17(8). 4961–4980. 15 indexed citations
12.
Pan, Xiaoliang, Junjie Yang, Richard Van, et al.. (2021). Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions. Journal of Chemical Theory and Computation. 17(9). 5745–5758. 85 indexed citations
13.
Shao, Yihan, et al.. (2021). Doubly Polarized QM/MM with Machine Learning Chaperone Polarizability. Journal of Chemical Theory and Computation. 17(12). 7682–7695. 7 indexed citations
14.
15.
Zhou, Yan, et al.. (2018). Mapping Free Energy Pathways for ATP Hydrolysis in the E. coli ABC Transporter HlyB by the String Method. Molecules. 23(10). 2652–2652. 10 indexed citations
16.
Zhou, Yan, et al.. (2016). Toward Determining ATPase Mechanism in ABC Transporters: Development of the Reaction Path–Force Matching QM/MM Method. PMC. 1 indexed citations
17.
Pu, Jingzhi & Martin Karplus. (2008). How subunit coupling produces the γ-subunit rotary motion in F 1 -ATPase. Proceedings of the National Academy of Sciences. 105(4). 1192–1197. 97 indexed citations
18.
Lin, Hai, Jingzhi Pu, Titus V. Albu, & Donald G. Truhlar. (2004). Efficient Molecular Mechanics for Chemical Reactions:  Multiconfiguration Molecular Mechanics Using Partial Electronic Structure Hessians. The Journal of Physical Chemistry A. 108(18). 4112–4124. 30 indexed citations
19.
20.
Pu, Jingzhi, J. C. Corchado, & Donald G. Truhlar. (2001). Test of variational transition state theory with multidimensional tunneling contributions against an accurate full-dimensional rate constant calculation for a six-atom system. The Journal of Chemical Physics. 115(13). 6266–6267. 48 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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