James Harry Moore
Impact in
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- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Materials Science 5
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- Computational Drug Discovery Methods 2
- Co-authors
- Gábor Cśanyi (2 shared papers)Joshua T. Horton (1 shared paper)Dávid Péter Kovács (1 shared paper)Ilyes Batatia (1 shared paper)Nicholas J. Browning (1 shared paper)Venkat Kapil (1 shared paper)William C. Witt (1 shared paper)Daniel J. Cole (1 shared paper)
- Journals
- Journal of the American Chemical Society (2 papers)The Journal of Chemical Physics (1 paper)Bioinformatics (1 paper)Advanced Synthesis & Catalysis (1 paper)Communications Chemistry (1 paper)
- Partner nations
- United KingdomUnited StatesSweden
In The Last Decade
James Harry Moore
7 papers receiving 453 citations
James Harry Moore's Hit Papers
Peers
Comparison fields: 5 of 43
- Materials Chemistry 290
- Computational Theory and Mathematics 64
- Catalysis 18
- Inorganic Chemistry 30
- Physical and Theoretical Chemistry 15
Countries citing papers authored by James Harry Moore
This map shows the geographic impact of James Harry Moore'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 James Harry Moore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Harry Moore more than expected).
Fields of papers citing papers by James Harry Moore
This network shows the impact of papers produced by James Harry Moore. 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 James Harry Moore. The network helps show where James Harry Moore may publish in the future.
Co-authors
The 20 scholars most cited alongside James Harry Moore, 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 | A foundation model for atomistic materials chemistry Hit paper breakdown → | 2023 | 195 |
| 2 | MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules Hit paper breakdown → | 2025 | 117 |
| 3 | A foundation model for atomistic materials chemistry Hit paper breakdown → | 2025 | 109 |
| 4 | 2023 | 21 | |
| 5 | 2022 | 11 | |
| 6 | 2022 | 6 | |
| 7 | 2026 | 3 |
About James Harry Moore
James Harry Moore is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Information Systems and Management, Organic Chemistry and Electrical and Electronic Engineering, having authored 7 papers that have together received 462 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (5 papers), Computational Drug Discovery Methods (2 papers), Catalytic C–H Functionalization Methods (1 paper), Sulfur-Based Synthesis Techniques (1 paper), Scientific Computing and Data Management (1 paper), Fuel Cells and Related Materials (1 paper), Synthesis and Catalytic Reactions (1 paper) and Protein Structure and Dynamics (1 paper). The work is most often cited by research in Materials Chemistry (290 citations), Computational Theory and Mathematics (64 citations), Catalysis (18 citations), Inorganic Chemistry (30 citations) and Physical and Theoretical Chemistry (15 citations). James Harry Moore has collaborated with scholars based in United Kingdom, United States and Sweden. Frequent co-authors include Gábor Cśanyi, Joshua T. Horton, Dávid Péter Kovács, Ilyes Batatia, Nicholas J. Browning, Venkat Kapil, William C. Witt, Daniel J. Cole, Ioan-Bogdan Magdău and Ola Engkvist. Their work appears in journals such as Journal of the American Chemical Society, The Journal of Chemical Physics, Bioinformatics, Advanced Synthesis & Catalysis and Communications Chemistry.
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.