Steven Kearnes
- Materials Chemistry top 5%
- Computational Theory and Mathematics top 0.5%
- Molecular Biology top 10%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Co-authors
- Patrick RileyVijay S. PandeKevin McCloskeyMarc BerndlBing HuangO. Anatole von LilienfeldJustin GilmerLuke A. D. Hutchison
- Topics
- Machine Learning in Materials Science (7 papers)Computational Drug Discovery Methods (7 papers)Chemical Synthesis and Analysis (3 papers)
- Partner nations
- United StatesGhanaPoland
In The Last Decade
Steven Kearnes
16 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 120
- Materials Chemistry 1.2k
- Computational Theory and Mathematics 1.1k
- Molecular Biology 723
- Artificial Intelligence 224
- Biomedical Engineering 145
Countries citing papers authored by Steven Kearnes
This map shows the geographic impact of Steven Kearnes'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 Steven Kearnes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven Kearnes more than expected).
Fields of papers citing papers by Steven Kearnes
This network shows the impact of papers produced by Steven Kearnes. 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 Steven Kearnes. The network helps show where Steven Kearnes may publish in the future.
Co-authorship network of co-authors of Steven Kearnes
This figure shows the co-authorship network connecting the top 25 collaborators of Steven Kearnes. A scholar is included among the top collaborators of Steven Kearnes 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 Steven Kearnes. Steven Kearnes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 23 | |
| 3 | 23 | |
| 4 | 179 | |
| 5 | Towards understanding retrosynthesis by energy-based models | 13 |
| 6 | 1 | |
| 7 | 5 | |
| 8 | 85 | |
| 9 | Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy | 5 |
| 10 | Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Errorbreakdown → | 459 |
| 11 | Molecular graph convolutions: moving beyond fingerprintsbreakdown → | 995 |
| 12 | 2 | |
| 13 | 28 | |
| 14 | 1 | |
| 15 | 2 | |
| 16 | 2 | |
| 17 | 2 |
About Steven Kearnes
Steven Kearnes is a scholar working on Computational Theory and Mathematics, Physical and Theoretical Chemistry and Information Systems and Management, having authored 17 papers that have together received 1.8k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (7 papers) and Chemical Synthesis and Analysis (3 papers). The work is most often cited by research in Computational Theory and Mathematics (1.1k citations), Materials Chemistry (1.2k citations) and Physical and Theoretical Chemistry (95 citations). Steven Kearnes has collaborated with scholars based in United States, Ghana and Poland. Frequent co-authors include Patrick Riley, Vijay S. Pande, Kevin McCloskey, Marc Berndl, Bing Huang, O. Anatole von Lilienfeld, Justin Gilmer, Luke A. D. Hutchison, George E. Dahl and Samuel S. Schoenholz. Their work appears in journals such as Journal of the American Chemical Society, Scientific Reports and Biophysical 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.