Yi‐Pei Li

3.3k total citations
53 papers, 1.4k citations indexed

About

Yi‐Pei Li is a scholar working on Materials Chemistry, Inorganic Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Yi‐Pei Li has authored 53 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Materials Chemistry, 15 papers in Inorganic Chemistry and 14 papers in Computational Theory and Mathematics. Recurrent topics in Yi‐Pei Li's work include Machine Learning in Materials Science (19 papers), Computational Drug Discovery Methods (13 papers) and Metal-Organic Frameworks: Synthesis and Applications (9 papers). Yi‐Pei Li is often cited by papers focused on Machine Learning in Materials Science (19 papers), Computational Drug Discovery Methods (13 papers) and Metal-Organic Frameworks: Synthesis and Applications (9 papers). Yi‐Pei Li collaborates with scholars based in Taiwan, United States and Australia. Yi‐Pei Li's co-authors include Alexis T. Bell, Martin Head‐Gordon, William H. Green, Colin A. Grambow, Shih‐Cheng Li, Shaama Mallikarjun Sharada, Barbara Pernici, Gabriele Scalia, Kevin C.‐W. Wu and Adeel Jamal and has published in prestigious journals such as Journal of the American Chemical Society, Angewandte Chemie International Edition and Nature Communications.

In The Last Decade

Yi‐Pei Li

48 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yi‐Pei Li Taiwan 20 743 407 331 330 269 53 1.4k
Niels Hansen Germany 25 912 1.2× 334 0.8× 617 1.9× 161 0.5× 251 0.9× 90 2.1k
Aditya Nandy United States 25 1.6k 2.2× 178 0.4× 855 2.6× 462 1.4× 136 0.5× 56 2.2k
Robert Pollice Canada 18 791 1.1× 199 0.5× 181 0.5× 335 1.0× 332 1.2× 39 1.7k
Jon Paul Janet Sweden 23 1.6k 2.1× 227 0.6× 528 1.6× 781 2.4× 91 0.3× 45 2.2k
Richard H. West United States 25 1.1k 1.5× 400 1.0× 62 0.2× 249 0.8× 325 1.2× 47 2.3k
Yanfei Guan United States 16 546 0.7× 159 0.4× 230 0.7× 333 1.0× 497 1.8× 24 1.4k
Chenru Duan United States 25 1.4k 1.9× 134 0.3× 371 1.1× 567 1.7× 121 0.4× 60 2.0k
Andrew F. Zahrt United States 13 598 0.8× 210 0.5× 273 0.8× 342 1.0× 481 1.8× 18 1.3k
Yan Yan Li China 26 473 0.6× 213 0.5× 228 0.7× 652 2.0× 121 0.4× 77 2.1k
Thomas W. Chamberlain United Kingdom 32 1.8k 2.5× 905 2.2× 301 0.9× 150 0.5× 1.0k 3.7× 114 3.2k

Countries citing papers authored by Yi‐Pei Li

Since Specialization
Citations

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

Fields of papers citing papers by Yi‐Pei Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yi‐Pei Li

This figure shows the co-authorship network connecting the top 25 collaborators of Yi‐Pei Li. A scholar is included among the top collaborators of Yi‐Pei Li 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 Yi‐Pei Li. Yi‐Pei Li 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.
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Yu, Ming‐Hsuan, Bi‐Hsuan Lin, Chih‐Wei Chu, et al.. (2025). Enhancing Charge Transport and Fast Non‐Volatile Memory in 2D Tin‐Based Perovskite Transistors through Porphyrin‐like Additives. Advanced Functional Materials. 36(4). 1 indexed citations
3.
Chen, Shen‐Ming, et al.. (2025). FeSn-integrated carbon nitride electrocatalyst for sensitive detection of pesticide dichlone in environmental samples. Journal of environmental chemical engineering. 13(5). 117559–117559. 1 indexed citations
4.
5.
Chen, Yingyu, Pin‐Yan Lee, P. Sakthivel, et al.. (2025). Hydrangea-like high-entropy material (FeNiCoMnMo)S2 as highly efficient bifunctional electrocatalyst for overall water splitting. Journal of Colloid and Interface Science. 707. 139662–139662.
6.
Li, Yi‐Pei, et al.. (2025). Uncertainty quantification with graph neural networks for efficient molecular design. Nature Communications. 16(1). 3262–3262. 11 indexed citations
7.
Muthiah, Balaganesh, et al.. (2024). Support effect in metal–organic framework-derived copper-based electrocatalysts facilitating the reduction of nitrate to ammonia. Electrochimica Acta. 492. 144348–144348. 8 indexed citations
8.
Kao, Y. w., Y. Wang, Shih‐Cheng Li, et al.. (2024). Tailoring parameters for QM/MM simulations: accurate modeling of adsorption and catalysis in zirconium-based metal–organic frameworks. Physical Chemistry Chemical Physics. 26(30). 20388–20398. 1 indexed citations
9.
Li, Yi‐Pei, et al.. (2024). Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions. Journal of Cheminformatics. 16(1). 11–11. 13 indexed citations
10.
Li, Yi‐Pei, et al.. (2024). AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry. Journal of Cheminformatics. 16(1). 74–74. 11 indexed citations
11.
Yu, Ming‐Hsuan, et al.. (2024). Unraveling Differences in the Effects of Ammonium/Amine‐Based Additives on the Performance and Stability of Inverted Perovskite Solar Cells. Small Methods. 8(12). e2400039–e2400039. 2 indexed citations
12.
Lin, Shiang‐Tai, et al.. (2024). Advancing vapor pressure prediction: A machine learning approach with directed message passing neural networks. Journal of the Taiwan Institute of Chemical Engineers. 105926–105926. 5 indexed citations
13.
Chen, Ching‐Tien, et al.. (2023). Reductive amination of furfural and furfurylamine with methoxides and MIL-53-NH2(Al)-derived Ru catalyst. Journal of the Taiwan Institute of Chemical Engineers. 158. 104884–104884. 3 indexed citations
14.
Muthiah, Balaganesh, Shih‐Cheng Li, & Yi‐Pei Li. (2023). Developing machine learning models for accurate prediction of radiative efficiency of greenhouse gases. Journal of the Taiwan Institute of Chemical Engineers. 151. 105123–105123. 15 indexed citations
15.
Matsagar, Babasaheb M., Chi Văn Nguyên, Md. Shahriar A. Hossain, et al.. (2023). Furfural hydrogenation into tetrahydrofurfuryl alcohol under ambient conditions: Role of Ni-supported catalysts and hydrogen source. Industrial Crops and Products. 195. 116390–116390. 18 indexed citations
16.
Matsagar, Babasaheb M., Yucheng Wang, Chi‐How Peng, et al.. (2023). Biomass-Based Discrete Furan Oligomers as Materials for Electrochromic Devices. ACS Sustainable Chemistry & Engineering. 12(1). 459–469. 6 indexed citations
17.
Lin, Tsai-Yu, Yen‐Chun Lin, Hyun Jung Yu, et al.. (2023). Mixed-linker strategy for suppressing structural flexibility of metal-organic framework membranes for gas separation. Communications Chemistry. 6(1). 118–118. 16 indexed citations
18.
Li, Yi‐Pei, et al.. (2023). Explainable uncertainty quantifications for deep learning-based molecular property prediction. Journal of Cheminformatics. 15(1). 13–13. 43 indexed citations
19.
Li, Xuemin, Yi‐Pei Li, Patrick H.‐L. Sit, et al.. (2021). Unveiling the elusive role of tetraethyl orthosilicate hydrolysis in ionic-liquid-templated zeolite synthesis. Materials Today Chemistry. 23. 100658–100658. 5 indexed citations
20.
Grambow, Colin A., Adeel Jamal, Yi‐Pei Li, et al.. (2017). Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods. Journal of the American Chemical Society. 140(3). 1035–1048. 87 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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