Jun Jiang

About Jun Jiang

Jun Jiang, With an exceptional h-index of 63 and a recent h-index of 55 (since 2020), a distinguished researcher at University of Science and Technology of China, specializes in the field of Theoretical Chemistry, Physical Chemistry, Photocatalysis/Catalysis, Material Design.

His recent articles reflect a diverse array of research interests and contributions to the field:

A machine learning protocol for geometric information retrieval from molecular spectra

Engineering Spin Polarization of the Surface-Adsorbed Fe Atom by Intercalating a Transition Metal Atom into the MoS2 Bilayer for Enhanced Nitrogen Reduction

Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist

Transferring graph neural network models for predicting bond dissociation energy between datasets

Manipulating the Spin State of Co Sites in Metal–Organic Frameworks for Boosting CO2 Photoreduction

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics

Slow-release synthesis of Cu single-atom catalysts with the optimized geometric structure and density of state distribution for Fenton-like catalysis

Inverse design of chiral functional films by a robotic AI-guided system

Jun Jiang Information

University

Position

___

Citations(all)

15696

Citations(since 2020)

11801

Cited By

8240

hIndex(all)

63

hIndex(since 2020)

55

i10Index(all)

191

i10Index(since 2020)

151

Email

University Profile Page

Google Scholar

Jun Jiang Skills & Research Interests

Theoretical Chemistry

Physical Chemistry

Photocatalysis/Catalysis

Material Design

Top articles of Jun Jiang

A machine learning protocol for geometric information retrieval from molecular spectra

Artificial Intelligence Chemistry

2024/6/1

Engineering Spin Polarization of the Surface-Adsorbed Fe Atom by Intercalating a Transition Metal Atom into the MoS2 Bilayer for Enhanced Nitrogen Reduction

JACS Au

2024/3/22

Transferring graph neural network models for predicting bond dissociation energy between datasets

Chinese Journal of Chemical Physics

2024/2/1

Manipulating the Spin State of Co Sites in Metal–Organic Frameworks for Boosting CO2 Photoreduction

Journal of the American Chemical Society

2024/1/26

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics

Journal of the American Chemical Society

2024/1/19

Slow-release synthesis of Cu single-atom catalysts with the optimized geometric structure and density of state distribution for Fenton-like catalysis

Proceedings of the National Academy of Sciences

2023/10/24

Promoting Oxygen Reduction Reaction on Carbon‐Based Materials by Selective Hydrogen Bonding

ChemSusChem

2023/8/21

Fusion of multiple spectra for investigating chemical bonding properties via machine learning

The Journal of Physical Chemistry Letters

2023/8/14

Machine Learning Descriptors for Data‐Driven Catalysis Study

2023/8

Promoting oxygen reduction reaction by inducing out‐of‐plane polarization in a metal phthalocyanine catalyst

Advanced Materials

2023/4/19

Starting the new journal of “Artificial Intelligence Chemistry”

2023/6/1

Interpretable catalysis models using machine learning with spectroscopic descriptors

ACS Catalysis

2023/5/18

Machine learning of spectra-property relationship for imperfect and small chemistry data

Proceedings of the National Academy of Sciences

2023/5/16

Self-optimized ligand effect of single-atom modifier in ternary Pt-based alloy for efficient hydrogen oxidation

Nano Letters

2023/4/28

Theoretical Investigation of Electrocatalytic Reduction of Nitrates to Ammonia on Highly Efficient and Selective g-C2N Monolayer-Supported Single Transition-Metal …

The Journal of Physical Chemistry Letters

2023/4/28

Spin Manipulation in a Metal–Organic Layer through Mechanical Exfoliation for Highly Selective CO2 Photoreduction

Angewandte Chemie

2023/4/24

See List of Professors in Jun Jiang University(University of Science and Technology of China)