Ilyes Batatia
- Materials Chemistry
- Computational Theory and Mathematics top 10%
- Electrical and Electronic Engineering
- Atomic and Molecular Physics, and Optics
- Molecular Biology
- Co-authors
- Gábor CśanyiDávid Péter KovácsVenkat KapilNicholas J. BrowningDaniel J. ColeIoan-Bogdan MagdăuJoshua T. HortonWilliam C. Witt
- Topics
- Machine Learning in Materials Science (6 papers)Computational Drug Discovery Methods (3 papers)Protein Structure and Dynamics (2 papers)
- Journals
- Journal of the American Chemical SocietyPhysical Review LettersThe Journal of Chemical Physics
- Partner nations
- United KingdomFranceUnited States
In The Last Decade
Ilyes Batatia
6 papers receiving 208 citations
Hit Papers
Peers
Comparison fields: 5 of 43
- Materials Chemistry 176
- Computational Theory and Mathematics 60
- Electrical and Electronic Engineering 37
- Atomic and Molecular Physics, and Optics 25
- Molecular Biology 24
Countries citing papers authored by Ilyes Batatia
This map shows the geographic impact of Ilyes Batatia'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 Ilyes Batatia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ilyes Batatia more than expected).
Fields of papers citing papers by Ilyes Batatia
This network shows the impact of papers produced by Ilyes Batatia. 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 Ilyes Batatia. The network helps show where Ilyes Batatia may publish in the future.
Co-authorship network of co-authors of Ilyes Batatia
This figure shows the co-authorship network connecting the top 25 collaborators of Ilyes Batatia. A scholar is included among the top collaborators of Ilyes Batatia based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ilyes Batatia. Ilyes Batatia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Moleculesbreakdown → | 48 |
| 2 | 19 | |
| 3 | 15 | |
| 4 | 26 | |
| 5 | Evaluation of the MACE force field architecture: From medicinal chemistry to materials sciencebreakdown → | 103 |
| 6 | 3 |
About Ilyes Batatia
Ilyes Batatia is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Inorganic Chemistry, having authored 6 papers that have together received 214 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (6 papers), Computational Drug Discovery Methods (3 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Materials Chemistry (176 citations), Computational Theory and Mathematics (60 citations) and Catalysis (11 citations). Ilyes Batatia has collaborated with scholars based in United Kingdom, France and United States. Frequent co-authors include Gábor Cśanyi, Dávid Péter Kovács, Venkat Kapil, Nicholas J. Browning, Daniel J. Cole, Ioan-Bogdan Magdău, Joshua T. Horton, William C. Witt, A. Miguel and J. Harry Moore. Their work appears in journals such as Journal of the American Chemical Society, Physical Review Letters and The Journal of Chemical Physics.
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.