C. Sievers
Impact in
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- Thermal properties of materials
- Machine Learning in Materials Science
- Graphene research and applications
- Advanced Thermoelectric Materials and Devices
- 2D Materials and Applications
- X-ray Diffraction in Crystallography
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- Thermal Radiation and Cooling Technologies
Papers in
-
- Graphene research and applications 3
- Machine Learning in Materials Science 3
- Thermal properties of materials 3
- Advanced Thermoelectric Materials and Devices 2
- X-ray Diffraction in Crystallography 2
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- Computational Drug Discovery Methods 2
- Co-authors
- Davide Donadio (4 shared papers)Eric Pop (2 shared papers)Mitchell Wood (2 shared papers)Aidan P. Thompson (2 shared papers)Victoria Chen (1 shared paper)Kenneth E. Goodson (1 shared paper)Shunda Chen (2 shared papers)Kirby K. H. Smithe (1 shared paper)
- Journals
- 2D Materials (1 paper)ACS Nano (1 paper)Journal of Chemical Theory and Computation (1 paper)Journal of Computational Physics (1 paper)The Journal of Open Source Software (1 paper)
- Partner nations
- United StatesSouth Korea
In The Last Decade
C. Sievers
5 papers receiving 96 citations
Peers
Comparison fields: 5 of 33
- Materials Chemistry 86
- Civil and Structural Engineering 19
- Computational Theory and Mathematics 12
- Surfaces, Coatings and Films 4
- Atomic and Molecular Physics, and Optics 17
Countries citing papers authored by C. Sievers
This map shows the geographic impact of C. Sievers'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 C. Sievers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. Sievers more than expected).
Fields of papers citing papers by C. Sievers
This network shows the impact of papers produced by C. Sievers. 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 C. Sievers. The network helps show where C. Sievers may publish in the future.
Co-authors
The 25 scholars most cited alongside C. Sievers, 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 | 2021 | 31 | |
| 2 | 2023 | 30 | |
| 3 | 2018 | 17 | |
| 4 | 2022 | 14 | |
| 5 | 2024 | 6 | |
| 6 | 2018 | 0 |
About C. Sievers
C. Sievers is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Physical and Theoretical Chemistry, Spectroscopy and Electrical and Electronic Engineering, having authored 6 papers that have together received 98 indexed citations. Recurring topics across this work include Graphene research and applications (3 papers), Machine Learning in Materials Science (3 papers), Thermal properties of materials (3 papers), Advanced Thermoelectric Materials and Devices (2 papers), Computational Drug Discovery Methods (2 papers), X-ray Diffraction in Crystallography (2 papers), Mass Spectrometry Techniques and Applications (1 paper) and Various Chemistry Research Topics (1 paper). The work is most often cited by research in Materials Chemistry (86 citations), Civil and Structural Engineering (19 citations), Computational Theory and Mathematics (12 citations), Surfaces, Coatings and Films (4 citations) and Atomic and Molecular Physics, and Optics (17 citations). C. Sievers has collaborated with scholars based in United States and South Korea. Frequent co-authors include Davide Donadio, Eric Pop, Mitchell Wood, Aidan P. Thompson, Victoria Chen, Kenneth E. Goodson, Shunda Chen, Kirby K. H. Smithe, Yong Cheol Shin and Zekun Chen. Their work appears in journals such as 2D Materials, ACS Nano, Journal of Chemical Theory and Computation, Journal of Computational Physics and The Journal of Open Source Software.
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.