Farhad Ramezanghorbani

617 total citations
7 papers, 336 citations indexed

About

Farhad Ramezanghorbani is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Farhad Ramezanghorbani has authored 7 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Materials Chemistry, 3 papers in Molecular Biology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Farhad Ramezanghorbani's work include Machine Learning in Materials Science (4 papers), Protein Structure and Dynamics (3 papers) and Computational Drug Discovery Methods (3 papers). Farhad Ramezanghorbani is often cited by papers focused on Machine Learning in Materials Science (4 papers), Protein Structure and Dynamics (3 papers) and Computational Drug Discovery Methods (3 papers). Farhad Ramezanghorbani collaborates with scholars based in United States, Japan and Iran. Farhad Ramezanghorbani's co-authors include Olexandr Isayev, Justin S. Smith, Xiang Gao, Adrián E. Roitberg, Robert Abel, Karl Leswing, Leif D. Jacobson, James Stevenson, Edward Harder and Delaram Ghoreishi and has published in prestigious journals such as The Journal of Chemical Physics, Chemistry of Materials and The Journal of Physical Chemistry B.

In The Last Decade

Farhad Ramezanghorbani

7 papers receiving 336 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Farhad Ramezanghorbani United States 5 252 135 108 60 54 7 336
Xingyi Guan United States 8 224 0.9× 122 0.9× 91 0.8× 40 0.7× 50 0.9× 12 331
Alice E. A. Allen United States 9 313 1.2× 119 0.9× 109 1.0× 48 0.8× 83 1.5× 15 466
Julia Westermayr Germany 10 263 1.0× 107 0.8× 76 0.7× 47 0.8× 130 2.4× 19 371
Mojtaba Haghighatlari United States 11 316 1.3× 163 1.2× 183 1.7× 58 1.0× 36 0.7× 15 476
Pavan Kumar Behara United States 9 206 0.8× 136 1.0× 154 1.4× 32 0.5× 42 0.8× 11 363
Qiyuan Zhao United States 13 188 0.7× 142 1.1× 128 1.2× 30 0.5× 34 0.6× 27 365
Riccardo Petraglia Switzerland 7 263 1.0× 170 1.3× 131 1.2× 47 0.8× 41 0.8× 10 439
Zun Wang China 11 232 0.9× 73 0.5× 79 0.7× 60 1.0× 45 0.8× 15 372
Jinxiao Zhang China 12 278 1.1× 94 0.7× 87 0.8× 78 1.3× 77 1.4× 29 481
Jonathan Vandermause United States 7 285 1.1× 76 0.6× 64 0.6× 68 1.1× 38 0.7× 8 349

Countries citing papers authored by Farhad Ramezanghorbani

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Farhad Ramezanghorbani

This figure shows the co-authorship network connecting the top 25 collaborators of Farhad Ramezanghorbani. A scholar is included among the top collaborators of Farhad Ramezanghorbani 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 Farhad Ramezanghorbani. Farhad Ramezanghorbani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

7 of 7 papers shown
1.
Matsuzawa, Nobuyuki, Keisuke Hayashi, Mohammad Atif Faiz Afzal, et al.. (2024). Exploring Molecules with Low Viscosity: Using Physics-Based Simulations and De Novo Design by Applying Reinforcement Learning. Chemistry of Materials. 36(23). 11706–11716. 1 indexed citations
2.
Jacobson, Leif D., James Stevenson, Farhad Ramezanghorbani, et al.. (2022). Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions. Journal of Chemical Theory and Computation. 18(4). 2354–2366. 42 indexed citations
3.
Agarwal, Garvit, James Stevenson, Leif D. Jacobson, et al.. (2022). High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations. The Journal of Physical Chemistry B. 126(33). 6271–6280. 52 indexed citations
4.
Gao, Xiang, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, & Adrián E. Roitberg. (2020). TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials. Journal of Chemical Information and Modeling. 60(7). 3408–3415. 216 indexed citations
5.
Ramezanghorbani, Farhad, Ping Lin, & Coray M. Colina. (2018). Optimizing Protein–Polymer Interactions in a Poly(ethylene glycol) Coarse-Grained Model. The Journal of Physical Chemistry B. 122(33). 7997–8005. 18 indexed citations
6.
Ramezanghorbani, Farhad, et al.. (2017). A multi-state coarse grained modeling approach for an intrinsically disordered peptide. The Journal of Chemical Physics. 147(9). 94103–94103. 5 indexed citations
7.
Ramezanghorbani, Farhad, et al.. (2014). Conference Room Reverberation Time Correction Using Helmholtz Resonators Lined with Absorbers. Shock and Vibration. 2014. 1–5. 2 indexed citations

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.

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2026