Steven K. Kauwe
- Materials Chemistry top 5%
- Computational Theory and Mathematics top 5%
- Electrical and Electronic Engineering
- Mechanical Engineering
- Biomedical Engineering
- Co-authors
- Taylor D. SparksRyan MurdockJake GraserAnthony WangJakoah BrgochAnton O. OliynykKristin A. PerssonAleksander Gurlo
- Topics
- Machine Learning in Materials Science (12 papers)Computational Drug Discovery Methods (8 papers)X-ray Diffraction in Crystallography (7 papers)
- Journals
- SHILAP Revista de lepidopterologíaChemistry of MaterialsInorganic Chemistry
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Steven K. Kauwe
15 papers receiving 944 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Materials Chemistry 743
- Computational Theory and Mathematics 185
- Electrical and Electronic Engineering 156
- Mechanical Engineering 153
- Biomedical Engineering 107
Countries citing papers authored by Steven K. Kauwe
This map shows the geographic impact of Steven K. Kauwe'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 K. Kauwe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven K. Kauwe more than expected).
Fields of papers citing papers by Steven K. Kauwe
This network shows the impact of papers produced by Steven K. Kauwe. 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 K. Kauwe. The network helps show where Steven K. Kauwe may publish in the future.
Co-authorship network of co-authors of Steven K. Kauwe
This figure shows the co-authorship network connecting the top 25 collaborators of Steven K. Kauwe. A scholar is included among the top collaborators of Steven K. Kauwe 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 K. Kauwe. Steven K. Kauwe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 140 | |
| 2 | 4 | |
| 3 | 10 | |
| 4 | 11 | |
| 5 | 2 | |
| 6 | Machine Learning for Materials Scientists: An Introductory Guide toward Best Practicesbreakdown → | 323 |
| 7 | 7 | |
| 8 | 20 | |
| 9 | 30 | |
| 10 | 56 | |
| 11 | 43 | |
| 12 | 80 | |
| 13 | 159 | |
| 14 | 8 | |
| 15 | 77 |
About Steven K. Kauwe
Steven K. Kauwe is a scholar working on Computational Theory and Mathematics, Metals and Alloys and Materials Chemistry, having authored 15 papers that have together received 970 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (12 papers), Computational Drug Discovery Methods (8 papers) and X-ray Diffraction in Crystallography (7 papers). The work is most often cited by research in Materials Chemistry (743 citations), Computational Theory and Mathematics (185 citations) and Metals and Alloys (22 citations). Steven K. Kauwe has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Taylor D. Sparks, Ryan Murdock, Jake Graser, Anthony Wang, Jakoah Brgoch, Anton O. Oliynyk, Kristin A. Persson, Aleksander Gurlo, Marcus Parry and Aria Mansouri Tehrani. Their work appears in journals such as SHILAP Revista de lepidopterología, Chemistry of Materials and Inorganic Chemistry.
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.