Steven B. Torrisi
- Materials Chemistry top 10%
- Electrical and Electronic Engineering
- Atomic and Molecular Physics, and Optics
- Renewable Energy, Sustainability and the Environment
- Biomedical Engineering
- Co-authors
- Efthimios KaxirasPaul CazeauxStephen CarrDaniel MassattMitchell LuskinJoseph H. MontoyaSantosh K. SuramLinda Hung
- Topics
- Machine Learning in Materials Science (12 papers)Electronic and Structural Properties of Oxides (6 papers)Electrocatalysts for Energy Conversion (3 papers)
- Journals
- Journal of the American Chemical SocietyNature CommunicationsEnergy & Environmental Science
- Partner nations
- United StatesSwitzerlandDenmark
In The Last Decade
Steven B. Torrisi
21 papers receiving 602 citations
Peers
Comparison fields: 5 of 75
- Materials Chemistry 463
- Electrical and Electronic Engineering 171
- Atomic and Molecular Physics, and Optics 119
- Renewable Energy, Sustainability and the Environment 92
- Biomedical Engineering 61
Countries citing papers authored by Steven B. Torrisi
This map shows the geographic impact of Steven B. Torrisi'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 B. Torrisi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven B. Torrisi more than expected).
Fields of papers citing papers by Steven B. Torrisi
This network shows the impact of papers produced by Steven B. Torrisi. 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 B. Torrisi. The network helps show where Steven B. Torrisi may publish in the future.
Co-authorship network of co-authors of Steven B. Torrisi
This figure shows the co-authorship network connecting the top 25 collaborators of Steven B. Torrisi. A scholar is included among the top collaborators of Steven B. Torrisi 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 B. Torrisi. Steven B. Torrisi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 3 | |
| 4 | 3 | |
| 5 | 18 | |
| 6 | 23 | |
| 7 | 5 | |
| 8 | 12 | |
| 9 | 25 | |
| 10 | 8 | |
| 11 | 15 | |
| 12 | 68 | |
| 13 | 31 | |
| 14 | 21 | |
| 15 | 30 | |
| 16 | 133 | |
| 17 | 5 | |
| 18 | Accelerating atomistic modelling with active learning | 1 |
| 19 | 193 | |
| 20 | 11 |
About Steven B. Torrisi
Steven B. Torrisi is a scholar working on Materials Chemistry, Catalysis and Renewable Energy, Sustainability and the Environment, having authored 21 papers that have together received 619 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (12 papers), Electronic and Structural Properties of Oxides (6 papers) and Electrocatalysts for Energy Conversion (3 papers). The work is most often cited by research in Materials Chemistry (463 citations), Catalysis (50 citations) and Renewable Energy, Sustainability and the Environment (92 citations). Steven B. Torrisi has collaborated with scholars based in United States, Switzerland and Denmark. Frequent co-authors include Efthimios Kaxiras, Paul Cazeaux, Stephen Carr, Daniel Massatt, Mitchell Luskin, Joseph H. Montoya, Santosh K. Suram, Linda Hung, Brian A. Rohr and Yang Ha. Their work appears in journals such as Journal of the American Chemical Society, Nature Communications and Energy & Environmental Science.
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.