Kate Huddleston
- Materials Chemistry
- Computational Theory and Mathematics top 5%
- Molecular Biology
- Atomic and Molecular Physics, and Optics
- Physical and Theoretical Chemistry
- Co-authors
- Adrián E. RoitbergJustin S. SmithR.I. ZubatyukChristian DevereuxKipton BarrosOlexandr IsayevMarcelo A. MartíDarío A. Estrı́n
- Topics
- Machine Learning in Materials Science (3 papers)Computational Drug Discovery Methods (2 papers)Innovative Microfluidic and Catalytic Techniques Innovation (1 paper)
- Journals
- Journal of Chemical Theory and ComputationJournal of Chemical Information and ModelingThe Yale Law Journal
- Partner nations
- United StatesArgentina
In The Last Decade
Kate Huddleston
3 papers receiving 283 citations
Hit Papers
Peers
Comparison fields: 5 of 54
- Materials Chemistry 218
- Computational Theory and Mathematics 164
- Molecular Biology 132
- Atomic and Molecular Physics, and Optics 38
- Physical and Theoretical Chemistry 27
Countries citing papers authored by Kate Huddleston
This map shows the geographic impact of Kate Huddleston'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 Kate Huddleston with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kate Huddleston more than expected).
Fields of papers citing papers by Kate Huddleston
This network shows the impact of papers produced by Kate Huddleston. 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 Kate Huddleston. The network helps show where Kate Huddleston may publish in the future.
Co-authorship network of co-authors of Kate Huddleston
This figure shows the co-authorship network connecting the top 25 collaborators of Kate Huddleston. A scholar is included among the top collaborators of Kate Huddleston 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 Kate Huddleston. Kate Huddleston is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 0 | |
| 3 | Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogensbreakdown → | 273 |
| 4 | Border Checkpoints and Substantive Due Process: Abortion Rights in the Border Zone | 2 |
About Kate Huddleston
Kate Huddleston is a scholar working on Information Systems and Management, Computational Theory and Mathematics and Reproductive Medicine, having authored 4 papers that have together received 285 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Computational Drug Discovery Methods (2 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (1 paper). The work is most often cited by research in Computational Theory and Mathematics (164 citations), Materials Chemistry (218 citations) and Physical and Theoretical Chemistry (27 citations). Kate Huddleston has collaborated with scholars based in United States and Argentina. Frequent co-authors include Adrián E. Roitberg, Justin S. Smith, R.I. Zubatyuk, Christian Devereux, Kipton Barros, Olexandr Isayev, Marcelo A. Martí, Darío A. Estrı́n, Mariano C. González Lebrero and Jiangeng Xue. Their work appears in journals such as Journal of Chemical Theory and Computation, Journal of Chemical Information and Modeling and The Yale Law Journal.
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.