Sam Cox
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
- Materials Chemistry top 10%
- Machine Learning in Materials Science
Papers in
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- Machine Learning in Materials Science 4
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- Scientific Computing and Data Management 1
- Co-authors
- Andrew Dickson White (4 shared papers)Andres M Bran (1 shared paper)Oliver Tobias Schilter (1 shared paper)Carlo Baldassari (1 shared paper)Philippe Schwaller (1 shared paper)Yuvraj Singh (1 shared paper)Ziyue Yang (1 shared paper)Glen M. Hocky (1 shared paper)
- Journals
- Nature Machine Intelligence (1 paper)Machine Learning Science and Technology (1 paper)Journal of Chemical Theory and Computation (1 paper)Digital Discovery (2 papers)PubMed (1 paper)
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Sam Cox
6 papers receiving 689 citations
Sam Cox's Hit Papers
Peers
Comparison fields: 5 of 78
- Health Informatics 34
- Materials Chemistry 385
- Computational Theory and Mathematics 130
- Artificial Intelligence 152
- Chemical Health and Safety 3
Countries citing papers authored by Sam Cox
This map shows the geographic impact of Sam Cox'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 Sam Cox with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Cox more than expected).
Fields of papers citing papers by Sam Cox
This network shows the impact of papers produced by Sam Cox. 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 Sam Cox. The network helps show where Sam Cox may publish in the future.
Co-authors
The 10 scholars most cited alongside Sam Cox, 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 | Augmenting large language models with chemistry tools Hit paper breakdown → | 2024 | 439 |
| 2 | 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon Hit paper breakdown → | 2023 | 158 |
| 3 | 2023 | 86 | |
| 4 | 2023 | 24 | |
| 5 | 2026 | 7 | |
| 6 | 2022 | 4 |
About Sam Cox
Sam Cox is a scholar working on Materials Chemistry, Information Systems and Management, Physical and Theoretical Chemistry, Artificial Intelligence and Computational Theory and Mathematics, having authored 6 papers that have together received 718 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (4 papers), Artificial Intelligence Applications (2 papers), Protein Structure and Dynamics (1 paper), Artificial Intelligence in Education (1 paper), Advanced Physical and Chemical Molecular Interactions (1 paper), Software Engineering Research (1 paper), Computational Drug Discovery Methods (1 paper) and Scientific Computing and Data Management (1 paper). The work is most often cited by research in Health Informatics (34 citations), Materials Chemistry (385 citations), Computational Theory and Mathematics (130 citations), Artificial Intelligence (152 citations) and Chemical Health and Safety (3 citations). Sam Cox has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Andrew Dickson White, Andres M Bran, Oliver Tobias Schilter, Carlo Baldassari, Philippe Schwaller, Yuvraj Singh, Ziyue Yang, Glen M. Hocky, Heta A. Gandhi and Geemi P. Wellawatte. Their work appears in journals such as Nature Machine Intelligence, Machine Learning Science and Technology, Journal of Chemical Theory and Computation, Digital Discovery and PubMed.
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.