Ryan Cohn
Impact in
- Structural Biology top 10%
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
Papers in
-
- X-ray Diffraction in Crystallography 2
- Machine Learning in Materials Science 2
-
- Image Processing and 3D Reconstruction 1
- Co-authors
- Elizabeth A. Holm (5 shared papers)Simon J. L. Billinge (1 shared paper)Francesca Tavazza (1 shared paper)Chi Chen (1 shared paper)Brian DeCost (1 shared paper)Chris Wolverton (1 shared paper)Ankit Agrawal (1 shared paper)Kamal Choudhary (1 shared paper)
- Journals
- Integrating materials and manufacturing innovation (1 paper)Annual Review of Materials Research (1 paper)JOM (1 paper)npj Computational Materials (1 paper)Advanced Engineering Materials (1 paper)
- Partner nations
- United StatesGermanyRussia
In The Last Decade
Ryan Cohn
6 papers receiving 776 citations
Ryan Cohn's Hit Papers
Peers
Comparison fields: 5 of 122
- Structural Biology 15
- Materials Chemistry 408
- Metals and Alloys 18
- Computational Theory and Mathematics 73
- Surfaces, Coatings and Films 28
Countries citing papers authored by Ryan Cohn
This map shows the geographic impact of Ryan Cohn'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 Ryan Cohn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan Cohn more than expected).
Fields of papers citing papers by Ryan Cohn
This network shows the impact of papers produced by Ryan Cohn. 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 Ryan Cohn. The network helps show where Ryan Cohn may publish in the future.
Co-authors
The 16 scholars most cited alongside Ryan Cohn, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Recent advances and applications of deep learning methods in materials science Hit paper breakdown → | 2022 | 701 |
| 2 | 2021 | 66 | |
| 3 | 2023 | 20 | |
| 4 | 2018 | 2 | |
| 5 | 2024 | 1 | |
| 6 | 2021 | 1 |
About Ryan Cohn
Ryan Cohn is a scholar working on Materials Chemistry, Computer Vision and Pattern Recognition, Mechanical Engineering, Artificial Intelligence and Fluid Flow and Transfer Processes, having authored 6 papers that have together received 791 indexed citations. Recurring topics across this work include X-ray Diffraction in Crystallography (2 papers), Machine Learning in Materials Science (2 papers), Mineral Processing and Grinding (1 paper), Aluminum Alloy Microstructure Properties (1 paper), Image Processing and 3D Reconstruction (1 paper), Rheology and Fluid Dynamics Studies (1 paper), Polymer crystallization and properties (1 paper) and Aluminum Alloys Composites Properties (1 paper). The work is most often cited by research in Structural Biology (15 citations), Materials Chemistry (408 citations), Metals and Alloys (18 citations), Computational Theory and Mathematics (73 citations) and Surfaces, Coatings and Films (28 citations). Ryan Cohn has collaborated with scholars based in United States, Germany and Russia. Frequent co-authors include Elizabeth A. Holm, Simon J. L. Billinge, Francesca Tavazza, Chi Chen, Brian DeCost, Chris Wolverton, Ankit Agrawal, Kamal Choudhary, Alok Choudhary and Shyue Ping Ong. Their work appears in journals such as Integrating materials and manufacturing innovation, Annual Review of Materials Research, JOM, npj Computational Materials and Advanced Engineering Materials.
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.