Andrew Rohskopf
- Materials Chemistry
- Civil and Structural Engineering top 5%
- Electrical and Electronic Engineering
- Mechanical Engineering
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Asegun HenryKiarash GordizKevin L. SchulteMyles A. SteinerColin C. KelsallEric J. TervoRyan M. FranceEvelyn N. Wang
- Topics
- Machine Learning in Materials Science (11 papers)Thermal properties of materials (8 papers)X-ray Diffraction in Crystallography (4 papers)
- Partner nations
- United StatesJapanGermany
In The Last Decade
Andrew Rohskopf
20 papers receiving 672 citations
Hit Papers
Peers
Comparison fields: 5 of 66
- Materials Chemistry 318
- Civil and Structural Engineering 220
- Electrical and Electronic Engineering 182
- Mechanical Engineering 157
- Statistical and Nonlinear Physics 91
Countries citing papers authored by Andrew Rohskopf
This map shows the geographic impact of Andrew Rohskopf'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 Andrew Rohskopf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Rohskopf more than expected).
Fields of papers citing papers by Andrew Rohskopf
This network shows the impact of papers produced by Andrew Rohskopf. 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 Andrew Rohskopf. The network helps show where Andrew Rohskopf may publish in the future.
Co-authorship network of co-authors of Andrew Rohskopf
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Rohskopf. A scholar is included among the top collaborators of Andrew Rohskopf based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andrew Rohskopf. Andrew Rohskopf is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 6 | |
| 5 | 30 | |
| 6 | 6 | |
| 7 | 9 | |
| 8 | 15 | |
| 9 | 10 | |
| 10 | Thermophotovoltaic efficiency of 40%breakdown → | 219 |
| 11 | 44 | |
| 12 | 12 | |
| 13 | 67 | |
| 14 | 11 | |
| 15 | 61 | |
| 16 | 8 | |
| 17 | 11 | |
| 18 | 140 | |
| 19 | 5 | |
| 20 | 36 |
About Andrew Rohskopf
Andrew Rohskopf is a scholar working on Materials Chemistry, General Materials Science and Automotive Engineering, having authored 20 papers that have together received 695 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (11 papers), Thermal properties of materials (8 papers) and X-ray Diffraction in Crystallography (4 papers). The work is most often cited by research in Civil and Structural Engineering (220 citations), Statistical and Nonlinear Physics (91 citations) and Materials Chemistry (318 citations). Andrew Rohskopf has collaborated with scholars based in United States, Japan and Germany. Frequent co-authors include Asegun Henry, Kiarash Gordiz, Kevin L. Schulte, Myles A. Steiner, Colin C. Kelsall, Eric J. Tervo, Ryan M. France, Evelyn N. Wang, Daniel J. Friedman and Alina LaPotin. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences 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.