Mingyu Wan

666 citations
11 papers · 517 · 1 hit paper · h-index 9

Impact in

Papers in

Mingyu Wan

11 papers receiving 508 citations

Mingyu Wan's Hit Papers

Bridging the complexity gap in computational heterogeneous catalysis with machine learning 2023 · 165 citations
1650+1+2Years since publication50100150

Peers

Mingyu Wan
Comparison fields: 5 of 44
  • Catalysis 255
  • Renewable Energy, Sustainability and the Environment 365
  • Process Chemistry and Technology 62
  • Electrochemistry 36
  • Materials Chemistry 231
Replace Shiyu Zhen with:
Shiyu Zhen China
Arifin Luthfi Maulana Indonesia
Saurabh Bhandari United States
Stephen E. Weitzner United States
Rune Christensen Denmark
Kyle D. Cummins United States
Kristopher Brown United States
Aram Yoon Germany
Makoto Urushihara Japan
Elizabeth R. Corson United States
Mingyu Wan relative to Shiyu Zhen China Shiyu Zhen's profile →
Citations per field
00.5×3.5×
Shiyu Zhen · 1×
Citations per year

Countries citing papers authored by Mingyu Wan

Since Specialization
Citations

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

Fields of papers citing papers by Mingyu Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mingyu Wan. 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 Mingyu Wan. The network helps show where Mingyu Wan may publish in the future.

Co-authors

The 25 scholars most cited alongside Mingyu Wan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mingyu Wan Line = papers co-authored together Mingyu Wan links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Hit paper breakdown →
2023165
2 2021154
3 202382
4 202244
5 202325
6 202514
7 20259
8 20239
9 20248
10 20216
11 20221

About Mingyu Wan

Mingyu Wan is a scholar working on Renewable Energy, Sustainability and the Environment, Catalysis, Materials Chemistry, Electrical and Electronic Engineering and Computer Networks and Communications, having authored 11 papers that have together received 517 indexed citations. Recurring topics across this work include CO2 Reduction Techniques and Catalysts (7 papers), Electrocatalysts for Energy Conversion (5 papers), Ionic liquids properties and applications (5 papers), Machine Learning in Materials Science (2 papers), Advanced Thermoelectric Materials and Devices (2 papers), Magnetism in coordination complexes (1 paper), Caching and Content Delivery (1 paper) and Catalysis and Oxidation Reactions (1 paper). The work is most often cited by research in Catalysis (255 citations), Renewable Energy, Sustainability and the Environment (365 citations), Process Chemistry and Technology (62 citations), Electrochemistry (36 citations) and Materials Chemistry (231 citations). Mingyu Wan has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Fanglin Che, Hemanth Somarajan Pillai, Hongliang Xin, Siwen Wang, Tianyou Mou, Neil M. Schweitzer, Xue Han, Han Yue, Hongfu Liu and Yi Xu. Their work appears in journals such as ChemCatChem, Nature Communications, Nature Chemistry, Journal of the American Chemical Society and The Journal of Physical Chemistry C.

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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