Pei‐Lin Kang

1.1k total citations
16 papers, 790 citations indexed

About

Pei‐Lin Kang is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Pei‐Lin Kang has authored 16 papers receiving a total of 790 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Materials Chemistry, 5 papers in Molecular Biology and 5 papers in Computational Theory and Mathematics. Recurrent topics in Pei‐Lin Kang's work include Machine Learning in Materials Science (12 papers), Computational Drug Discovery Methods (5 papers) and Protein Structure and Dynamics (3 papers). Pei‐Lin Kang is often cited by papers focused on Machine Learning in Materials Science (12 papers), Computational Drug Discovery Methods (5 papers) and Protein Structure and Dynamics (3 papers). Pei‐Lin Kang collaborates with scholars based in China, Italy and United Kingdom. Pei‐Lin Kang's co-authors include Zhi‐Pan Liu, Cheng Shang, Sida Huang, Yunfei Shi, Michele Parrinello, Zhengxin Yang, Sicong Ma, P. Hu, Yefei Li and Umberto Raucci and has published in prestigious journals such as Journal of the American Chemical Society, SHILAP Revista de lepidopterología and Accounts of Chemical Research.

In The Last Decade

Pei‐Lin Kang

16 papers receiving 770 citations

Peers

Countries citing papers authored by Pei‐Lin Kang

Since Specialization

Citations

This map shows the geographic impact of Pei‐Lin Kang'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 Pei‐Lin Kang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pei‐Lin Kang more than expected).

Fields of papers citing papers by Pei‐Lin Kang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pei‐Lin Kang. 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 Pei‐Lin Kang. The network helps show where Pei‐Lin Kang may publish in the future.

Co-authorship network of co-authors of Pei‐Lin Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Pei‐Lin Kang. A scholar is included among the top collaborators of Pei‐Lin Kang 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 Pei‐Lin Kang. Pei‐Lin Kang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

#	Work	Indexed citations
1	A Machine Learning-Driven, Probability-Based Approach to Enzyme Catalysis 2025 ·ACS Catalysis ·(unknown), Umberto Raucci, (unknown), Pei‐Lin Kang, Rui P. P. Neves, Maria J. Ramos, Michele Parrinello	2
2	Everything everywhere all at once: a probability-based enhanced sampling approach to rare events 2025 ·Nature Computational Science ·(unknown), Pei‐Lin Kang, Michele Parrinello	8
3	LASP to the Future of Atomic Simulation: Intelligence and Automation 2024 ·SHILAP Revista de lepidopterología ·(unknown), Zhengxin Yang, (unknown), Yunfei Shi, Pei‐Lin Kang, Sicong Ma, Yefei Li, Cheng Shang, Zhi‐Pan Liu	14
4	Computing the committor with the committor to study the transition state ensemble 2024 ·Nature Computational Science ·Pei‐Lin Kang, (unknown), Michele Parrinello	32
5	Many-Body Function Corrected Neural Network with Atomic Attention (MBNN-att) for Molecular Property Prediction 2024 ·Journal of Chemical Theory and Computation ·Zhengxin Yang, (unknown), Pei‐Lin Kang, (unknown), Cheng Shang, Zhi‐Pan Liu	8
6	Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface 2023 ·Journal of Chemical Theory and Computation ·Pei‐Lin Kang, Zhengxin Yang, Cheng Shang, Zhi‐Pan Liu	18
7	Machine Learning for Chemistry: Basics and Applications 2023 ·Engineering ·Yunfei Shi, Zhengxin Yang, Sicong Ma, Pei‐Lin Kang, Cheng Shang, P. Hu, Zhi‐Pan Liu	46
8	Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity 2022 ·Chemical Science ·Pei‐Lin Kang, Yunfei Shi, Cheng Shang, Zhi‐Pan Liu	22
9	Methanol Synthesis from CO₂/CO Mixture on Cu–Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search 2022 ·Journal of the American Chemical Society ·Yunfei Shi, Pei‐Lin Kang, Cheng Shang, Zhi‐Pan Liu	92
10	Active Site of Catalytic Ethene Epoxidation: Machine-Learning Global Pathway Sampling Rules Out the Metal Sites 2021 ·ACS Catalysis ·(unknown), Pei‐Lin Kang, Zhi‐Pan Liu	30
11	Recent implementations in LASP 3.0: Global neural network potential with multiple elements and better long-range description 2021 ·Chinese Journal of Chemical Physics ·Pei‐Lin Kang, Cheng Shang, Zhi‐Pan Liu	25
12	Reaction prediction via atomistic simulation: from quantum mechanics to machine learning 2020 ·iScience ·Pei‐Lin Kang, Zhi‐Pan Liu	33
13	Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration 2020 ·Accounts of Chemical Research ·Pei‐Lin Kang, Cheng Shang, Zhi‐Pan Liu	97
14	LASP: Fast global potential energy surface exploration 2019 ·Wiley Interdisciplinary Reviews Computational Molecular Science ·Sida Huang, Cheng Shang, Pei‐Lin Kang, (unknown), Zhi‐Pan Liu	171
15	Glucose to 5-Hydroxymethylfurfural: Origin of Site-Selectivity Resolved by Machine Learning Based Reaction Sampling 2019 ·Journal of the American Chemical Society ·Pei‐Lin Kang, Cheng Shang, Zhi‐Pan Liu	84
16	Atomic structure of boron resolved using machine learning and global sampling 2018 ·Chemical Science ·Sida Huang, Cheng Shang, Pei‐Lin Kang, Zhi‐Pan Liu	108

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.

Explore authors with similar magnitude of impact