Michael J. Willatt
Impact in
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- Computational Drug Discovery Methods
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
Papers in
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- Machine Learning in Materials Science 7
- X-ray Diffraction in Crystallography 4
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- Computational Drug Discovery Methods 6
- Co-authors
- Michele Ceriotti (8 shared papers)Félix Musil (4 shared papers)Stuart C. Althorpe (3 shared papers)Albert P. Bartók (2 shared papers)Christoph Ortner (2 shared papers)Sergey N. Pozdnyakov (2 shared papers)Gábor Cśanyi (2 shared papers)Timothy J. H. Hele (1 shared paper)
- Journals
- The Journal of Chemical Physics (6 papers)Journal of Chemical Theory and Computation (1 paper)Physical Review Letters (1 paper)Refubium (Universitätsbibliothek der Freien Universität Berlin) (1 paper)Apollo (University of Cambridge) (1 paper)
- Partner nations
- SwitzerlandUnited KingdomGermany
In The Last Decade
Michael J. Willatt
11 papers receiving 600 citations
Peers
Comparison fields: 5 of 54
- Computational Theory and Mathematics 211
- Materials Chemistry 426
- Atomic and Molecular Physics, and Optics 206
- Structural Biology 7
- Spectroscopy 76
Countries citing papers authored by Michael J. Willatt
This map shows the geographic impact of Michael J. Willatt'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 Michael J. Willatt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael J. Willatt more than expected).
Fields of papers citing papers by Michael J. Willatt
This network shows the impact of papers produced by Michael J. Willatt. 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 Michael J. Willatt. The network helps show where Michael J. Willatt may publish in the future.
Co-authors
The 14 scholars most cited alongside Michael J. Willatt, 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 | 131 | |
| 2 | 2019 | 126 | |
| 3 | 2019 | 109 | |
| 4 | 2015 | 87 | |
| 5 | 2021 | 45 | |
| 6 | 2019 | 41 | |
| 7 | 2022 | 32 | |
| 8 | 2018 | 29 | |
| 9 | 2022 | 7 | |
| 10 | Theory and Practice of Atom-Density Representations for Machine Learning | 2018 | 1 |
| 11 | 2024 | 1 |
About Michael J. Willatt
Michael J. Willatt is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Atomic and Molecular Physics, and Optics, Molecular Biology and Spectroscopy, having authored 11 papers that have together received 609 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (6 papers), X-ray Diffraction in Crystallography (4 papers), Spectroscopy and Quantum Chemical Studies (3 papers), Quantum, superfluid, helium dynamics (3 papers), Protein Structure and Dynamics (3 papers), Cold Atom Physics and Bose-Einstein Condensates (2 papers) and Molecular Junctions and Nanostructures (1 paper). The work is most often cited by research in Computational Theory and Mathematics (211 citations), Materials Chemistry (426 citations), Atomic and Molecular Physics, and Optics (206 citations), Structural Biology (7 citations) and Spectroscopy (76 citations). Michael J. Willatt has collaborated with scholars based in Switzerland, United Kingdom and Germany. Frequent co-authors include Michele Ceriotti, Félix Musil, Stuart C. Althorpe, Albert P. Bartók, Christoph Ortner, Sergey N. Pozdnyakov, Gábor Cśanyi, Timothy J. H. Hele, Jigyasa Nigam and Till Junge. Their work appears in journals such as The Journal of Chemical Physics, Journal of Chemical Theory and Computation, Physical Review Letters, Refubium (Universitätsbibliothek der Freien Universität Berlin) and Apollo (University of Cambridge).
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.