Deepak Kamal
Impact in
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- Computational Drug Discovery Methods
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Ferroelectric and Piezoelectric Materials
Papers in
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- Machine Learning in Materials Science 6
- Ferroelectric and Piezoelectric Materials 4
- High voltage insulation and dielectric phenomena 3
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- Dielectric materials and actuators 6
- Advanced Sensor and Energy Harvesting Materials 2
- Co-authors
- Rampi Ramprasad (12 shared papers)Lihua Chen (7 shared papers)Rohit Batra (3 shared papers)Anand Chandrasekaran (3 shared papers)Chiho Kim (3 shared papers)Tran Doan Huan (6 shared papers)Rishi Gurnani (3 shared papers)Yang Cao (8 shared papers)
- Journals
- ACS Applied Materials & Interfaces (4 papers)Nature Communications (1 paper)Machine Learning Science and Technology (1 paper)The Journal of Chemical Physics (1 paper)Chemistry of Materials (1 paper)
- Partner nations
- United StatesJapanChina
In The Last Decade
Deepak Kamal
12 papers receiving 693 citations
Peers
Comparison fields: 5 of 70
- Computational Theory and Mathematics 176
- Materials Chemistry 510
- Polymers and Plastics 94
- Biomedical Engineering 190
- Catalysis 29
Countries citing papers authored by Deepak Kamal
This map shows the geographic impact of Deepak Kamal'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 Deepak Kamal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepak Kamal more than expected).
Fields of papers citing papers by Deepak Kamal
This network shows the impact of papers produced by Deepak Kamal. 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 Deepak Kamal. The network helps show where Deepak Kamal may publish in the future.
Co-authors
The 25 scholars most cited alongside Deepak Kamal, 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 | 2019 | 228 | |
| 2 | 2020 | 196 | |
| 3 | 2021 | 63 | |
| 4 | 2024 | 54 | |
| 5 | 2021 | 45 | |
| 6 | 2021 | 31 | |
| 7 | 2020 | 22 | |
| 8 | 2020 | 20 | |
| 9 | 2021 | 17 | |
| 10 | 2022 | 12 | |
| 11 | 2021 | 7 | |
| 12 | 2025 | 5 |
About Deepak Kamal
Deepak Kamal is a scholar working on Materials Chemistry, Biomedical Engineering, Electrical and Electronic Engineering, Computational Theory and Mathematics and Polymers and Plastics, having authored 12 papers that have together received 700 indexed citations. Recurring topics across this work include Dielectric materials and actuators (6 papers), Machine Learning in Materials Science (6 papers), Ferroelectric and Piezoelectric Materials (4 papers), Computational Drug Discovery Methods (3 papers), High voltage insulation and dielectric phenomena (3 papers), Fuel Cells and Related Materials (2 papers), Advanced Sensor and Energy Harvesting Materials (2 papers) and Semiconductor materials and devices (2 papers). The work is most often cited by research in Computational Theory and Mathematics (176 citations), Materials Chemistry (510 citations), Polymers and Plastics (94 citations), Biomedical Engineering (190 citations) and Catalysis (29 citations). Deepak Kamal has collaborated with scholars based in United States, Japan and China. Frequent co-authors include Rampi Ramprasad, Lihua Chen, Rohit Batra, Anand Chandrasekaran, Chiho Kim, Tran Doan Huan, Rishi Gurnani, Yang Cao, Pranav Shetty and Chao Wu. Their work appears in journals such as ACS Applied Materials & Interfaces, Nature Communications, Machine Learning Science and Technology, The Journal of Chemical Physics and Chemistry of 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.