Jonathan Vandermause
- Materials Chemistry
- Computational Theory and Mathematics top 10%
- Electrical and Electronic Engineering
- Molecular Biology
- Renewable Energy, Sustainability and the Environment
- Co-authors
- Boris KozinskyYu XieJin Soo LimCameron J. OwenJonathan P. MailoaMordechai KornbluthCheol Woo ParkChris Wolverton
- Topics
- Machine Learning in Materials Science (7 papers)Protein Structure and Dynamics (4 papers)Advanced Materials Characterization Techniques (1 paper)
- Journals
- Journal of the American Chemical SocietyNature CommunicationsJournal of Chemical Theory and Computation
- Partner nations
- United StatesGermany
In The Last Decade
Jonathan Vandermause
8 papers receiving 343 citations
Peers
Comparison fields: 5 of 47
- Materials Chemistry 285
- Computational Theory and Mathematics 76
- Electrical and Electronic Engineering 68
- Molecular Biology 64
- Renewable Energy, Sustainability and the Environment 41
Countries citing papers authored by Jonathan Vandermause
This map shows the geographic impact of Jonathan Vandermause'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 Jonathan Vandermause with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Vandermause more than expected).
Fields of papers citing papers by Jonathan Vandermause
This network shows the impact of papers produced by Jonathan Vandermause. 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 Jonathan Vandermause. The network helps show where Jonathan Vandermause may publish in the future.
Co-authorship network of co-authors of Jonathan Vandermause
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan Vandermause. A scholar is included among the top collaborators of Jonathan Vandermause 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 Jonathan Vandermause. Jonathan Vandermause is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 42 | |
| 3 | 96 | |
| 4 | 31 | |
| 5 | 107 | |
| 6 | 57 | |
| 7 | Accelerating atomistic modelling with active learning | 1 |
| 8 | 6 |
About Jonathan Vandermause
Jonathan Vandermause is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics, having authored 8 papers that have together received 349 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Protein Structure and Dynamics (4 papers) and Advanced Materials Characterization Techniques (1 paper). The work is most often cited by research in Materials Chemistry (285 citations), Catalysis (37 citations) and Computational Theory and Mathematics (76 citations). Jonathan Vandermause has collaborated with scholars based in United States and Germany. Frequent co-authors include Boris Kozinsky, Yu Xie, Jin Soo Lim, Cameron J. Owen, Jonathan P. Mailoa, Mordechai Kornbluth, Cheol Woo Park, Chris Wolverton, Lixin Sun and Anders Johansson. Their work appears in journals such as Journal of the American Chemical Society, Nature Communications and Journal of Chemical Theory and Computation.
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.