Farhad Ramezanghorbani
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
Papers in
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- Machine Learning in Materials Science 4
- Enzyme Structure and Function 2
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- Protein Structure and Dynamics 3
- Protein purification and stability 1
- Co-authors
- Xiang Gao (1 shared paper)Adrián E. Roitberg (1 shared paper)Justin S. Smith (1 shared paper)Olexandr Isayev (1 shared paper)Leif D. Jacobson (2 shared papers)James Stevenson (2 shared papers)Robert Abel (2 shared papers)Karl Leswing (3 shared papers)
- Journals
- The Journal of Physical Chemistry B (2 papers)Shock and Vibration (1 paper)Journal of Chemical Information and Modeling (1 paper)Chemistry of Materials (1 paper)The Journal of Chemical Physics (1 paper)
- Partner nations
- United StatesIranTürkiye
In The Last Decade
Farhad Ramezanghorbani
7 papers receiving 360 citations
Peers
Comparison fields: 5 of 57
- Computational Theory and Mathematics 136
- Materials Chemistry 273
- Catalysis 22
- Physical and Theoretical Chemistry 28
- Atomic and Molecular Physics, and Optics 55
Countries citing papers authored by Farhad Ramezanghorbani
This map shows the geographic impact of Farhad Ramezanghorbani'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 Farhad Ramezanghorbani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Farhad Ramezanghorbani more than expected).
Fields of papers citing papers by Farhad Ramezanghorbani
This network shows the impact of papers produced by Farhad Ramezanghorbani. 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 Farhad Ramezanghorbani. The network helps show where Farhad Ramezanghorbani may publish in the future.
Co-authors
The 25 scholars most cited alongside Farhad Ramezanghorbani, 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 | 2020 | 228 | |
| 2 | 2022 | 58 | |
| 3 | 2022 | 47 | |
| 4 | 2018 | 18 | |
| 5 | 2017 | 5 | |
| 6 | 2014 | 2 | |
| 7 | 2024 | 2 |
About Farhad Ramezanghorbani
Farhad Ramezanghorbani is a scholar working on Materials Chemistry, Molecular Biology, Computational Theory and Mathematics, Electrical and Electronic Engineering and Control and Systems Engineering, having authored 7 papers that have together received 360 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (4 papers), Computational Drug Discovery Methods (3 papers), Protein Structure and Dynamics (3 papers), Enzyme Structure and Function (2 papers), Fuel Cells and Related Materials (2 papers), Supramolecular Self-Assembly in Materials (1 paper), Advanced Battery Materials and Technologies (1 paper) and Protein purification and stability (1 paper). The work is most often cited by research in Computational Theory and Mathematics (136 citations), Materials Chemistry (273 citations), Catalysis (22 citations), Physical and Theoretical Chemistry (28 citations) and Atomic and Molecular Physics, and Optics (55 citations). Farhad Ramezanghorbani has collaborated with scholars based in United States, Iran and Türkiye. Frequent co-authors include Xiang Gao, Adrián E. Roitberg, Justin S. Smith, Olexandr Isayev, Leif D. Jacobson, James Stevenson, Robert Abel, Karl Leswing, Edward Harder and Delaram Ghoreishi. Their work appears in journals such as The Journal of Physical Chemistry B, Shock and Vibration, Journal of Chemical Information and Modeling, Chemistry of Materials and The Journal of Chemical Physics.
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.