Muratahan Aykol
- Materials Chemistry top 0.5%
- Electrical and Electronic Engineering top 0.5%
- Automotive Engineering top 0.1%
- Mechanical Engineering top 1%
- Electronic, Optical and Magnetic Materials top 2%
- Co-authors
- Chris WolvertonScott KirklinJames E. SaalBryce MeredigPatrick K. HerringJeff W. DoakKristin A. PerssonStefan Rühl
- Topics
- Machine Learning in Materials Science (32 papers)Advancements in Battery Materials (25 papers)Advanced Battery Materials and Technologies (17 papers)
- Partner nations
- United StatesTürkiyeJapan
In The Last Decade
Muratahan Aykol
69 papers receiving 10.9k citations
Hit Papers
Peers
Comparison fields: 5 of 134
- Materials Chemistry 6.1k
- Electrical and Electronic Engineering 5.3k
- Automotive Engineering 3.1k
- Mechanical Engineering 1.4k
- Electronic, Optical and Magnetic Materials 1.1k
Countries citing papers authored by Muratahan Aykol
This map shows the geographic impact of Muratahan Aykol'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 Muratahan Aykol with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muratahan Aykol more than expected).
Fields of papers citing papers by Muratahan Aykol
This network shows the impact of papers produced by Muratahan Aykol. 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 Muratahan Aykol. The network helps show where Muratahan Aykol may publish in the future.
Co-authorship network of co-authors of Muratahan Aykol
This figure shows the co-authorship network connecting the top 25 collaborators of Muratahan Aykol. A scholar is included among the top collaborators of Muratahan Aykol 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 Muratahan Aykol. Muratahan Aykol is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 2 | |
| 3 | 4 | |
| 4 | 12 | |
| 5 | Scaling deep learning for materials discoverybreakdown → | 667 |
| 6 | 30 | |
| 7 | 15 | |
| 8 | 103 | |
| 9 | 30 | |
| 10 | 118 | |
| 11 | Closed-loop optimization of fast-charging protocols for batteries with machine learningbreakdown → | 748 |
| 12 | 85 | |
| 13 | 297 | |
| 14 | 31 | |
| 15 | 74 | |
| 16 | 57 | |
| 17 | 92 | |
| 18 | 259 | |
| 19 | 96 | |
| 20 | 2 |
About Muratahan Aykol
Muratahan Aykol is a scholar working on Materials Chemistry, Automotive Engineering and Catalysis, having authored 70 papers that have together received 11.2k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (32 papers), Advancements in Battery Materials (25 papers) and Advanced Battery Materials and Technologies (17 papers). The work is most often cited by research in Automotive Engineering (3.1k citations), Materials Chemistry (6.1k citations) and Electrical and Electronic Engineering (5.3k citations). Muratahan Aykol has collaborated with scholars based in United States, Türkiye and Japan. Frequent co-authors include Chris Wolverton, Scott Kirklin, James E. Saal, Bryce Meredig, Patrick K. Herring, Jeff W. Doak, Kristin A. Persson, Stefan Rühl, Alexander Thompson and Richard D. Braatz. Their work appears in journals such as Nature, Journal of the American Chemical Society and Nature Communications.
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.