Tomasz Badowski
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
Papers in
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- Machine Learning in Materials Science 7
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- Computational Drug Discovery Methods 6
- Co-authors
- Bartosz A. Grzybowski (9 shared papers)Ewa Gajewska (5 shared papers)Karol Molga (5 shared papers)Wiktor Beker (3 shared papers)Sara Szymkuć (4 shared papers)Piotr Dittwald (1 shared paper)Olga Staszewska‐Krajewska (1 shared paper)Jacek Młynarski (1 shared paper)
- Journals
- Angewandte Chemie International Edition (3 papers)Nature (1 paper)Chemical Science (1 paper)Wiley Interdisciplinary Reviews Computational Molecular Science (1 paper)Entropy (1 paper)
- Partner nations
- South KoreaPolandUnited States
In The Last Decade
Tomasz Badowski
10 papers receiving 535 citations
Peers
Comparison fields: 5 of 77
- Computational Theory and Mathematics 264
- Materials Chemistry 332
- Catalysis 34
- Inorganic Chemistry 46
- Molecular Biology 213
Countries citing papers authored by Tomasz Badowski
This map shows the geographic impact of Tomasz Badowski'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 Tomasz Badowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomasz Badowski more than expected).
Fields of papers citing papers by Tomasz Badowski
This network shows the impact of papers produced by Tomasz Badowski. 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 Tomasz Badowski. The network helps show where Tomasz Badowski may publish in the future.
Co-authors
The 17 scholars most cited alongside Tomasz Badowski, 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 | 2020 | 197 | |
| 2 | 2018 | 125 | |
| 3 | 2019 | 64 | |
| 4 | 2019 | 54 | |
| 5 | 2013 | 50 | |
| 6 | 2021 | 16 | |
| 7 | 2022 | 15 | |
| 8 | 2018 | 14 | |
| 9 | 2019 | 9 | |
| 10 | 2021 | 5 |
About Tomasz Badowski
Tomasz Badowski is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Molecular Biology, Physical and Theoretical Chemistry and Organic Chemistry, having authored 10 papers that have together received 549 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (6 papers), Chemical Synthesis and Analysis (3 papers), Various Chemistry Research Topics (2 papers), Advanced Thermodynamics and Statistical Mechanics (1 paper), AI-based Problem Solving and Planning (1 paper), Enzyme Catalysis and Immobilization (1 paper) and Plant biochemistry and biosynthesis (1 paper). The work is most often cited by research in Computational Theory and Mathematics (264 citations), Materials Chemistry (332 citations), Catalysis (34 citations), Inorganic Chemistry (46 citations) and Molecular Biology (213 citations). Tomasz Badowski has collaborated with scholars based in South Korea, Poland and United States. Frequent co-authors include Bartosz A. Grzybowski, Ewa Gajewska, Karol Molga, Wiktor Beker, Sara Szymkuć, Piotr Dittwald, Olga Staszewska‐Krajewska, Jacek Młynarski, Milan Mrksich and Oskar Popik. Their work appears in journals such as Angewandte Chemie International Edition, Nature, Chemical Science, Wiley Interdisciplinary Reviews Computational Molecular Science and Entropy.
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.