Adeesh Kolluru
Impact in
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- Catalysis and Oxidation Reactions
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- Machine Learning in Materials Science
- Catalytic Processes in Materials Science
- X-ray Diffraction in Crystallography
Papers in
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- Machine Learning in Materials Science 5
- Catalytic Processes in Materials Science 2
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- Topic Modeling 2
- Co-authors
- Muhammed Shuaibi (4 shared papers)Abhishek Das (3 shared papers)C. Lawrence Zitnick (3 shared papers)Zachary W. Ulissi (3 shared papers)Brandon M. Wood (2 shared papers)Siddharth Goyal (2 shared papers)Félix Therrien (1 shared paper)Anuroop Sriram (1 shared paper)
- Journals
- ACS Catalysis (2 papers)Proceedings of the National Academy of Sciences (1 paper)Catalysis Science & Technology (1 paper)The Journal of Chemical Physics (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Adeesh Kolluru
6 papers receiving 287 citations
Adeesh Kolluru's Hit Papers
Peers
Comparison fields: 5 of 43
- Catalysis 46
- Materials Chemistry 239
- Renewable Energy, Sustainability and the Environment 74
- Computational Theory and Mathematics 63
- Physical and Theoretical Chemistry 9
Countries citing papers authored by Adeesh Kolluru
This map shows the geographic impact of Adeesh Kolluru'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 Adeesh Kolluru with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adeesh Kolluru more than expected).
Fields of papers citing papers by Adeesh Kolluru
This network shows the impact of papers produced by Adeesh Kolluru. 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 Adeesh Kolluru. The network helps show where Adeesh Kolluru may publish in the future.
Co-authors
The 25 scholars most cited alongside Adeesh Kolluru, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts Hit paper breakdown → | 2023 | 205 |
| 2 | 2022 | 55 | |
| 3 | 2022 | 25 | |
| 4 | 2024 | 6 | |
| 5 | 2025 | 3 | |
| 6 | 2025 | 3 |
About Adeesh Kolluru
Adeesh Kolluru is a scholar working on Materials Chemistry, Artificial Intelligence, Electrical and Electronic Engineering, Computational Mechanics and Mechanical Engineering, having authored 6 papers that have together received 297 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (5 papers), Catalytic Processes in Materials Science (2 papers), Topic Modeling (2 papers), Ferroelectric and Negative Capacitance Devices (1 paper), Advanced Photocatalysis Techniques (1 paper), Ion-surface interactions and analysis (1 paper), Fuel Cells and Related Materials (1 paper) and Electrocatalysts for Energy Conversion (1 paper). The work is most often cited by research in Catalysis (46 citations), Materials Chemistry (239 citations), Renewable Energy, Sustainability and the Environment (74 citations), Computational Theory and Mathematics (63 citations) and Physical and Theoretical Chemistry (9 citations). Adeesh Kolluru has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Muhammed Shuaibi, Abhishek Das, C. Lawrence Zitnick, Zachary W. Ulissi, Brandon M. Wood, Siddharth Goyal, Félix Therrien, Anuroop Sriram, Oleksandr Voznyy and Edward H. Sargent. Their work appears in journals such as ACS Catalysis, Proceedings of the National Academy of Sciences, Catalysis Science & Technology, The Journal of Chemical Physics and DOAJ (DOAJ: Directory of Open Access Journals).
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.