Nitai Sylvetsky
Impact in
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- Crystallography and molecular interactions
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- Advanced Chemical Physics Studies
- Spectroscopy and Quantum Chemical Studies
Papers in
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- Advanced Chemical Physics Studies 8
- Spectroscopy and Quantum Chemical Studies 2
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- Machine Learning in Materials Science 4
- Porphyrin and Phthalocyanine Chemistry 2
- Catalytic Processes in Materials Science 1
- Co-authors
- Jan M. L. Martin (11 shared papers)Golokesh Santra (3 shared papers)Amir Karton (2 shared papers)Manoj K. Kesharwani (4 shared papers)Debashree Manna (2 shared papers)Kirk A. Peterson (1 shared paper)Mercedes Alonso (2 shared papers)Ambar Banerjee (2 shared papers)
In The Last Decade
Nitai Sylvetsky
12 papers receiving 886 citations
Nitai Sylvetsky's Hit Papers
Peers
Comparison fields: 5 of 68
- Physical and Theoretical Chemistry 160
- Atomic and Molecular Physics, and Optics 525
- Spectroscopy 195
- Organic Chemistry 239
- Catalysis 57
Countries citing papers authored by Nitai Sylvetsky
This map shows the geographic impact of Nitai Sylvetsky'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 Nitai Sylvetsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nitai Sylvetsky more than expected).
Fields of papers citing papers by Nitai Sylvetsky
This network shows the impact of papers produced by Nitai Sylvetsky. 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 Nitai Sylvetsky. The network helps show where Nitai Sylvetsky may publish in the future.
Co-authors
The 12 scholars most cited alongside Nitai Sylvetsky, 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 | Minimally Empirical Double-Hybrid Functionals Trained against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4 Hit paper breakdown → | 2019 | 380 |
| 2 | 2017 | 146 | |
| 3 | 2016 | 94 | |
| 4 | 2017 | 78 | |
| 5 | 2020 | 70 | |
| 6 | 2020 | 44 | |
| 7 | 2018 | 37 | |
| 8 | 2020 | 23 | |
| 9 | 2017 | 11 | |
| 10 | 2020 | 6 | |
| 11 | 2021 | 4 | |
| 12 | 2017 | 3 |
About Nitai Sylvetsky
Nitai Sylvetsky is a scholar working on Atomic and Molecular Physics, and Optics, Materials Chemistry, Organic Chemistry, Physical and Theoretical Chemistry and Spectroscopy, having authored 12 papers that have together received 896 indexed citations. Recurring topics across this work include Advanced Chemical Physics Studies (8 papers), Machine Learning in Materials Science (4 papers), Crystallography and molecular interactions (3 papers), Porphyrin and Phthalocyanine Chemistry (2 papers), Spectroscopy and Quantum Chemical Studies (2 papers), Advanced NMR Techniques and Applications (1 paper), Catalytic Processes in Materials Science (1 paper) and Marine and coastal ecosystems (1 paper). The work is most often cited by research in Physical and Theoretical Chemistry (160 citations), Atomic and Molecular Physics, and Optics (525 citations), Spectroscopy (195 citations), Organic Chemistry (239 citations) and Catalysis (57 citations). Nitai Sylvetsky has collaborated with scholars based in Israel, Belgium and Australia. Frequent co-authors include Jan M. L. Martin, Golokesh Santra, Amir Karton, Manoj K. Kesharwani, Debashree Manna, Kirk A. Peterson, Mercedes Alonso, Ambar Banerjee, Irena Efremenko and Minsik Cho. Their work appears in journals such as The Journal of Physical Chemistry A, Journal of Chemical Theory and Computation, Scientific Reports, Journal of Computational Chemistry and ChemPhysChem.
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.