Rampi Ramprasad
- Materials Chemistry top 0.1%
- Electrical and Electronic Engineering top 0.5%
- Biomedical Engineering top 0.5%
- Computational Theory and Mathematics top 0.1%
- Polymers and Plastics top 0.5%
- Co-authors
- Tran Doan HuanRohit BatraChiho KimGhanshyam PilaniaArun Mannodi‐KanakkithodiVenkatesh BotuLihua ChenAnand Chandrasekaran
- Topics
- Machine Learning in Materials Science (83 papers)Semiconductor materials and devices (59 papers)Dielectric materials and actuators (46 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of the American Chemical SocietyPhysical Review Letters
- Partner nations
- United StatesJapanCanada
In The Last Decade
Rampi Ramprasad
312 papers receiving 16.0k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Materials Chemistry 11.5k
- Electrical and Electronic Engineering 5.2k
- Biomedical Engineering 3.9k
- Computational Theory and Mathematics 2.0k
- Polymers and Plastics 1.9k
Countries citing papers authored by Rampi Ramprasad
This map shows the geographic impact of Rampi Ramprasad'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 Rampi Ramprasad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rampi Ramprasad more than expected).
Fields of papers citing papers by Rampi Ramprasad
This network shows the impact of papers produced by Rampi Ramprasad. 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 Rampi Ramprasad. The network helps show where Rampi Ramprasad may publish in the future.
Co-authorship network of co-authors of Rampi Ramprasad
This figure shows the co-authorship network connecting the top 25 collaborators of Rampi Ramprasad. A scholar is included among the top collaborators of Rampi Ramprasad 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 Rampi Ramprasad. Rampi Ramprasad is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 2 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 4 | |
| 8 | 22 | |
| 9 | 13 | |
| 10 | 34 | |
| 11 | 38 | |
| 12 | polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informaticsbreakdown → | 129 |
| 13 | 2 | |
| 14 | 35 | |
| 15 | 45 | |
| 16 | 11 | |
| 17 | 6 | |
| 18 | 66 | |
| 19 | 68 | |
| 20 | 44 |
About Rampi Ramprasad
Rampi Ramprasad is a scholar working on Materials Chemistry, Polymers and Plastics and Computational Theory and Mathematics, having authored 320 papers that have together received 16.3k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (83 papers), Semiconductor materials and devices (59 papers) and Dielectric materials and actuators (46 papers). The work is most often cited by research in Materials Chemistry (11.5k citations), Polymers and Plastics (1.9k citations) and Computational Theory and Mathematics (2.0k citations). Rampi Ramprasad has collaborated with scholars based in United States, Japan and Canada. Frequent co-authors include Tran Doan Huan, Rohit Batra, Chiho Kim, Ghanshyam Pilania, Arun Mannodi‐Kanakkithodi, Venkatesh Botu, Lihua Chen, Anand Chandrasekaran, Gregory A. Sotzing and Vinit Sharma. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Physical Review Letters.
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.