Jingzhi Pu

Associate Professor

Indiana University-Purdue University Indianapolis

United States

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Jingzhi Pu is an Associate Professor at Indiana University – Purdue University Indianapolis, USA. His research areas include plasmonic nanostructures, machine learning in chemical systems, and advanced computational methods for enzymatic reactions. Recent publications highlight his work on sensitive detection methods, enhancements in molecular simulation tools, and innovative approaches in the design of molecular systems.

Articles (17)

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2025

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2021

Collaborators (10)

Jose C. Corchado

SPAIN

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Jing Zhang

Rensselaer Polytechnic Institute

UNITED STATES

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Amedeo Caflisch

University of Zurich

SWITZERLAND

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Andrew Chesney

University of Oklahoma

UNITED STATES

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Jordi Villà-Freixa

Full Professor

Universitat de Vic - Universitat Central de Catalunya

SPAIN

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Wonmuk Hwang

Texas A&M University

UNITED STATES

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Yongming Deng

Assistant Professor

Indiana University-Purdue University Indianapolis

UNITED STATES

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Junming Ho

Associate Professor

University of New South Wales

AUSTRALIA

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Stefan Boresch

Universität Wien

AUSTRIA

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Yun Luo

department of biotechnology and pharmaceutical sciences western university of health sciences pomona ca usa

UNITED STATES

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Jingzhi Pu

Research Interests

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About

Articles (17)

Anisotropically Shaped Plasmonic WO3–x Nanostructure-Driven Ultrasensitive SERS Detection and Machine Learning-Based Differentiation of Nitro-Explosives

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed

Polyrate 2023: A computer program for the calculation of chemical reaction rates for polyatomics. New version announcement

Design of Light‐Induced Solid‐State Plasmonic Rulers via Tethering Photoswitchable Molecular Machines to Gold Nanostructures Displaying Angstrom Length Resolution

DFT/MM Simulations for Cycloreversion Reaction of Cyclobutane Pyrimidine Dimer with Deprotonated and Protonated E283

Training machine learning potentials for reactive systems: A Colab tutorial on basic models

Hybrid Metal–Ligand Interfacial Dipole Engineering of Functional Plasmonic Nanostructures for Extraordinary Responses of Optoelectronic Properties

Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions

Photoinduced Site-Selective Functionalization of Aliphatic C–H Bonds by Pyridine <i>N</i>-oxide Based HAT Catalysts

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

Facilitating ab initio QM/MM free energy simulations by Gaussian process regression with derivative observations

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions

Doubly Polarized QM/MM with Machine Learning Chaperone Polarizability

Reaction Path-Force Matching in Collective Variables: Determining Ab Initio QM/MM Free Energy Profiles by Fitting Mean Force

Interligand communication in a metal mediated LL′CT system – a case study

Collaborators (10)

Jose C. Corchado

Jing Zhang

Amedeo Caflisch

Andrew Chesney

Jordi Villà-Freixa

Wonmuk Hwang

Yongming Deng

Junming Ho

Stefan Boresch

Yun Luo

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