Nina Andrejevic
Impact in
- Structural Biology top 10%
- Process Chemistry and Technology top 10%
Papers in
-
- Machine Learning in Materials Science 7
- Graphene research and applications 4
- Thermal properties of materials 4
- Advanced Thermoelectric Materials and Devices 4
-
- Topological Materials and Phenomena 6
- Co-authors
- Mingda Li (15 shared papers)Elliot Padgett (3 shared papers)David A. Muller (3 shared papers)Thanh Nguyen (9 shared papers)Zhantao Chen (6 shared papers)Yi Jiang (2 shared papers)Wenbin Gu (1 shared paper)Zhongyi Liu (1 shared paper)
- Journals
- Advanced Materials (2 papers)Nature Communications (1 paper)iScience (1 paper)Advanced Science (1 paper)Nano Letters (1 paper)
- Partner nations
- United StatesGermanyJapan
In The Last Decade
Nina Andrejevic
19 papers receiving 509 citations
Peers
Comparison fields: 5 of 47
- Structural Biology 18
- Process Chemistry and Technology 32
- Renewable Energy, Sustainability and the Environment 123
- Materials Chemistry 322
- Radiation 34
Countries citing papers authored by Nina Andrejevic
This map shows the geographic impact of Nina Andrejevic'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 Nina Andrejevic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nina Andrejevic more than expected).
Fields of papers citing papers by Nina Andrejevic
This network shows the impact of papers produced by Nina Andrejevic. 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 Nina Andrejevic. The network helps show where Nina Andrejevic may publish in the future.
Co-authors
The 25 scholars most cited alongside Nina Andrejevic, 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 | 2018 | 132 | |
| 2 | 2021 | 82 | |
| 3 | 2015 | 67 | |
| 4 | 2020 | 67 | |
| 5 | 2020 | 36 | |
| 6 | 2022 | 23 | |
| 7 | Direct Prediction of Phonon Density of States With Euclidean Neural Networks. | 2021 | 22 |
| 8 | 2022 | 20 | |
| 9 | Theory of electron–phonon–dislon interacting system—toward a quantized theory of dislocations | 2017 | 15 |
| 10 | 2022 | 14 | |
| 11 | 2024 | 13 | |
| 12 | 2021 | 6 | |
| 13 | 2018 | 5 | |
| 14 | 2021 | 5 | |
| 15 | Discovery of Giant, Non-saturating Thermopower in Topological Semimetal at Quantum Limit | 2019 | 3 |
| 16 | 2022 | 3 | |
| 17 | 2021 | 2 | |
| 18 | 2016 | 1 | |
| 19 | 2022 | 1 | |
| 20 | 2024 | 0 |
About Nina Andrejevic
Nina Andrejevic is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics, Condensed Matter Physics, Structural Biology and Civil and Structural Engineering, having authored 20 papers that have together received 517 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Topological Materials and Phenomena (6 papers), Advanced Condensed Matter Physics (4 papers), Graphene research and applications (4 papers), Thermal properties of materials (4 papers), Advanced Thermoelectric Materials and Devices (4 papers), Thermal Radiation and Cooling Technologies (2 papers) and Advanced Electron Microscopy Techniques and Applications (2 papers). The work is most often cited by research in Structural Biology (18 citations), Process Chemistry and Technology (32 citations), Renewable Energy, Sustainability and the Environment (123 citations), Materials Chemistry (322 citations) and Radiation (34 citations). Nina Andrejevic has collaborated with scholars based in United States, Germany and Japan. Frequent co-authors include Mingda Li, Elliot Padgett, David A. Muller, Thanh Nguyen, Zhantao Chen, Yi Jiang, Wenbin Gu, Zhongyi Liu, Tess Smidt and Thomas E. Moylan. Their work appears in journals such as Advanced Materials, Nature Communications, iScience, Advanced Science and Nano Letters.
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.