Anand Chandrasekaran
- Materials Chemistry top 5%
- Machine Learning in Materials Science 20
- X-ray Diffraction in Crystallography 3
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- Computational Drug Discovery Methods 9
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- Neuroscience and Neuropharmacology Research 2
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- Molecular Junctions and Nanostructures 3
- Fuel Cells and Related Materials 3
- Cognitive Neuroscience top 5%
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- Innovative Microfluidic and Catalytic Techniques Innovation 4
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- Retinal Development and Disorders 3
Anand Chandrasekaran
32 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 125
- Materials Chemistry 1.5k
- Computational Theory and Mathematics 480
- Cellular and Molecular Neuroscience 513
- Electrical and Electronic Engineering 1.4k
- Cognitive Neuroscience 460
Countries citing papers authored by Anand Chandrasekaran
This map shows the geographic impact of Anand Chandrasekaran'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 Anand Chandrasekaran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anand Chandrasekaran more than expected).
Fields of papers citing papers by Anand Chandrasekaran
This network shows the impact of papers produced by Anand Chandrasekaran. 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 Anand Chandrasekaran. The network helps show where Anand Chandrasekaran may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Anand Chandrasekaran, 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 | 2025 | 0 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 40 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 6 | |
| 6 | 2024 | 5 | |
| 7 | 2023 | 0 | |
| 8 | 2022 | 1 | |
| 9 | 2022 | 15 | |
| 10 | 2021 | 1 | |
| 11 | 2020 | 182 | |
| 12 | 2020 | 19 | |
| 13 | 2019 | 116 | |
| 14 | 2019 | 176 | |
| 15 | 2019 | 18 | |
| 16 | 2018 | 76 | |
| 17 | Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulationsbreakdown → | 2014 | 847 |
| 18 | 2009 | 3 | |
| 19 | 2007 | 70 | |
| 20 | 2005 | 141 |
About Anand Chandrasekaran
Anand Chandrasekaran is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Metals and Alloys, having authored 34 papers that have together received 3.2k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (20 papers), Computational Drug Discovery Methods (9 papers), Innovative Microfluidic and Catalytic Techniques Innovation (4 papers), Molecular Junctions and Nanostructures (3 papers), Fuel Cells and Related Materials (3 papers), Retinal Development and Disorders (3 papers), X-ray Diffraction in Crystallography (3 papers) and Neuroscience and Neuropharmacology Research (2 papers). The work is most often cited by research in Materials Chemistry (1.5k citations), Computational Theory and Mathematics (480 citations) and Cellular and Molecular Neuroscience (513 citations). Anand Chandrasekaran has collaborated with scholars based in United States, India and Switzerland. Frequent co-authors include Rampi Ramprasad, Chiho Kim, Tran Doan Huan, Shruti Venkatram, Rohit Batra, Kwabena Boahen, John V. Arthur, Swadesh Choudhary, Jean-Marie Bussat and Paul Merolla. Their work appears in journals such as Journal of Neuroscience, Nano Letters and Journal of Applied 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.