William T. Darrow
Impact in
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- Computational Drug Discovery Methods
- Catalysis top 10%
- Catalysis and Oxidation Reactions
Papers in
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- Machine Learning in Materials Science 3
- Porphyrin and Phthalocyanine Chemistry 2
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- Catalysis and Oxidation Reactions 2
- Ammonia Synthesis and Nitrogen Reduction 1
- Co-authors
- Scott E. Denmark (3 shared papers)Jeremy Henle (3 shared papers)Andrew F. Zahrt (3 shared papers)Yang Wang (2 shared papers)Timothy D. Lash (2 shared papers)Alison R. Fout (2 shared papers)Marshall R. Brennan (1 shared paper)Gregory M. Ferrence (1 shared paper)
- Journals
- Journal of the American Chemical Society (1 paper)Reaction Chemistry & Engineering (1 paper)Journal of Porphyrins and Phthalocyanines (1 paper)Inorganic Chemistry (1 paper)Science (1 paper)
- Partner nations
- United States
In The Last Decade
William T. Darrow
7 papers receiving 614 citations
William T. Darrow's Hit Papers
Peers
Comparison fields: 5 of 62
- Computational Theory and Mathematics 210
- Catalysis 77
- Inorganic Chemistry 128
- Materials Chemistry 385
- Process Chemistry and Technology 17
Countries citing papers authored by William T. Darrow
This map shows the geographic impact of William T. Darrow'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 William T. Darrow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William T. Darrow more than expected).
Fields of papers citing papers by William T. Darrow
This network shows the impact of papers produced by William T. Darrow. 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 William T. Darrow. The network helps show where William T. Darrow may publish in the future.
Co-authors
The 8 scholars most cited alongside William T. Darrow, 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 | Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning Hit paper breakdown → | 2019 | 517 |
| 2 | 2020 | 67 | |
| 3 | 2021 | 16 | |
| 4 | 2021 | 12 | |
| 5 | 2019 | 10 | |
| 6 | 2017 | 3 | |
| 7 | 2021 | 2 |
About William T. Darrow
William T. Darrow is a scholar working on Materials Chemistry, Catalysis, Inorganic Chemistry, Molecular Biology and Computational Theory and Mathematics, having authored 7 papers that have together received 627 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Catalysis and Oxidation Reactions (2 papers), Computational Drug Discovery Methods (2 papers), Porphyrin and Phthalocyanine Chemistry (2 papers), Porphyrin Metabolism and Disorders (2 papers), Metal-Catalyzed Oxygenation Mechanisms (2 papers), Ammonia Synthesis and Nitrogen Reduction (1 paper) and Metal-Organic Frameworks: Synthesis and Applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (210 citations), Catalysis (77 citations), Inorganic Chemistry (128 citations), Materials Chemistry (385 citations) and Process Chemistry and Technology (17 citations). William T. Darrow has collaborated with scholars based in United States. Frequent co-authors include Scott E. Denmark, Jeremy Henle, Andrew F. Zahrt, Yang Wang, Timothy D. Lash, Alison R. Fout, Marshall R. Brennan and Gregory M. Ferrence. Their work appears in journals such as Journal of the American Chemical Society, Reaction Chemistry & Engineering, Journal of Porphyrins and Phthalocyanines, Inorganic Chemistry 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.