Ganesh Sivaraman
- Materials Chemistry top 10%
- Machine Learning in Materials Science 14
- X-ray Diffraction in Crystallography 7
- Graphene research and applications 7
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- Computational Drug Discovery Methods 3
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- Molecular Junctions and Nanostructures 3
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- Nanopore and Nanochannel Transport Studies 6
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- Speech and Audio Processing 3
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- Speech Recognition and Synthesis 3
- Co-authors
- Álvaro Vázquez‐MayagoitiaMaria FytaChris J. BenmoreAnand Narayanan KrishnamoorthyRodrigo G. AmorimChristian HolmGábor CśanyiNicholas E. Jackson
- Journals
- Physical Review Letters (1 paper)Advanced Materials (1 paper)The Journal of Chemical Physics (3 papers)
- Partner nations
- United StatesGermanyIndia
In The Last Decade
Ganesh Sivaraman
41 papers receiving 771 citations
Peers
Comparison fields: 5 of 91
- Materials Chemistry 539
- Computational Theory and Mathematics 119
- Fluid Flow and Transfer Processes 41
- Catalysis 34
- Electrical and Electronic Engineering 218
Countries citing papers authored by Ganesh Sivaraman
This map shows the geographic impact of Ganesh Sivaraman'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 Ganesh Sivaraman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ganesh Sivaraman more than expected).
Fields of papers citing papers by Ganesh Sivaraman
This network shows the impact of papers produced by Ganesh Sivaraman. 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 Ganesh Sivaraman. The network helps show where Ganesh Sivaraman may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ganesh Sivaraman, 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 | 2024 | 4 | |
| 3 | 2024 | 7 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 0 | |
| 7 | 2023 | 3 | |
| 8 | 2023 | 18 | |
| 9 | 2023 | 11 | |
| 10 | 2023 | 1 | |
| 11 | 2022 | 1 | |
| 12 | 2022 | 8 | |
| 13 | 2021 | 37 | |
| 14 | 2020 | 39 | |
| 15 | 2020 | 58 | |
| 16 | 2020 | 32 | |
| 17 | UV/vis absorption spectra database auto-generated for optical applications via the Argonne data science program | 2019 | 1 |
| 18 | 2019 | 74 | |
| 19 | 2016 | 36 | |
| 20 | 2016 | 13 |
About Ganesh Sivaraman
Ganesh Sivaraman is a scholar working on Structural Biology, General Dentistry and Materials Chemistry, having authored 45 papers that have together received 787 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (14 papers), X-ray Diffraction in Crystallography (7 papers), Graphene research and applications (7 papers), Nanopore and Nanochannel Transport Studies (6 papers), Speech and Audio Processing (3 papers), Speech Recognition and Synthesis (3 papers), Molecular Junctions and Nanostructures (3 papers) and Computational Drug Discovery Methods (3 papers). The work is most often cited by research in Materials Chemistry (539 citations), Computational Theory and Mathematics (119 citations) and Fluid Flow and Transfer Processes (41 citations). Ganesh Sivaraman has collaborated with scholars based in United States, Germany and India. Frequent co-authors include Álvaro Vázquez‐Mayagoitia, Maria Fyta, Chris J. Benmore, Anand Narayanan Krishnamoorthy, Rodrigo G. Amorim, Christian Holm, Gábor Cśanyi, Nicholas E. Jackson, Marius Stan and Ralph H. Scheicher. Their work appears in journals such as Physical Review Letters, Advanced 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.