Wiktor Beker
Impact in
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- Computational Drug Discovery Methods
- Materials Chemistry top 10%
- Machine Learning in Materials Science
Papers in
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- Protein Structure and Dynamics 7
- Chemical Synthesis and Analysis 5
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- Machine Learning in Materials Science 11
- Co-authors
- Bartosz A. Grzybowski (18 shared papers)Rafał Roszak (8 shared papers)Agnieszka Wołos (6 shared papers)Ewa Gajewska (4 shared papers)Sara Szymkuć (8 shared papers)Tomasz Badowski (3 shared papers)Karol Molga (5 shared papers)Martin D. Burke (3 shared papers)
- Journals
- Journal of the American Chemical Society (4 papers)Nature (2 papers)Angewandte Chemie International Edition (2 papers)Chemical Science (2 papers)Science (2 papers)
- Partner nations
- PolandSouth KoreaUnited States
In The Last Decade
Wiktor Beker
27 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 99
- Computational Theory and Mathematics 382
- Materials Chemistry 578
- Inorganic Chemistry 107
- Organic Chemistry 201
- Catalysis 47
Countries citing papers authored by Wiktor Beker
This map shows the geographic impact of Wiktor Beker'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 Wiktor Beker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wiktor Beker more than expected).
Fields of papers citing papers by Wiktor Beker
This network shows the impact of papers produced by Wiktor Beker. 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 Wiktor Beker. The network helps show where Wiktor Beker may publish in the future.
Co-authors
The 25 scholars most cited alongside Wiktor Beker, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 187 | |
| 2 | 2022 | 136 | |
| 3 | 2022 | 126 | |
| 4 | 2018 | 119 | |
| 5 | 2020 | 101 | |
| 6 | 2023 | 92 | |
| 7 | 2019 | 73 | |
| 8 | 2020 | 40 | |
| 9 | 2021 | 29 | |
| 10 | 2018 | 21 | |
| 11 | 2021 | 18 | |
| 12 | 2012 | 15 | |
| 13 | 2018 | 14 | |
| 14 | 2021 | 13 | |
| 15 | 2013 | 13 | |
| 16 | 2020 | 12 | |
| 17 | 2020 | 12 | |
| 18 | 2017 | 10 | |
| 19 | 2023 | 9 | |
| 20 | 2014 | 9 |
About Wiktor Beker
Wiktor Beker is a scholar working on Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, Organic Chemistry and Physical and Theoretical Chemistry, having authored 28 papers that have together received 1.1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (12 papers), Machine Learning in Materials Science (11 papers), Protein Structure and Dynamics (7 papers), Chemical Synthesis and Analysis (5 papers), Organic Chemistry Cycloaddition Reactions (2 papers), Surface Chemistry and Catalysis (2 papers), Microbial Natural Products and Biosynthesis (2 papers) and Free Radicals and Antioxidants (2 papers). The work is most often cited by research in Computational Theory and Mathematics (382 citations), Materials Chemistry (578 citations), Inorganic Chemistry (107 citations), Organic Chemistry (201 citations) and Catalysis (47 citations). Wiktor Beker has collaborated with scholars based in Poland, South Korea and United States. Frequent co-authors include Bartosz A. Grzybowski, Rafał Roszak, Agnieszka Wołos, Ewa Gajewska, Sara Szymkuć, Tomasz Badowski, Karol Molga, Martin D. Burke, Vandana Rathore and Nicholas H. Angello. Their work appears in journals such as Journal of the American Chemical Society, Nature, Angewandte Chemie International Edition, Chemical Science and Science.
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.