Kyle Bystrom
Impact in
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
- Advanced Thermoelectric Materials and Devices
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Materials Science 6
- X-ray Diffraction in Crystallography 3
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- Surface and Thin Film Phenomena 1
- Co-authors
- Mark Asta (2 shared papers)Kristin A. Persson (2 shared papers)Alireza Faghaninia (1 shared paper)Maxwell Dylla (1 shared paper)Ian Foster (1 shared paper)Nils Zimmermann (1 shared paper)Qi Wang (1 shared paper)Saurabh Bajaj (1 shared paper)
- Journals
- npj Computational Materials (2 papers)Computational Materials Science (1 paper)Physical review. B. (1 paper)The Journal of Physical Chemistry Letters (1 paper)Journal of Chemical Theory and Computation (1 paper)
- Partner nations
- United StatesNorwayBelgium
In The Last Decade
Kyle Bystrom
6 papers receiving 795 citations
Kyle Bystrom's Hit Papers
Peers
Comparison fields: 5 of 64
- Materials Chemistry 684
- Computational Theory and Mathematics 127
- Metals and Alloys 19
- Catalysis 50
- Surfaces, Coatings and Films 25
Countries citing papers authored by Kyle Bystrom
This map shows the geographic impact of Kyle Bystrom'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 Kyle Bystrom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyle Bystrom more than expected).
Fields of papers citing papers by Kyle Bystrom
This network shows the impact of papers produced by Kyle Bystrom. 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 Kyle Bystrom. The network helps show where Kyle Bystrom may publish in the future.
Co-authors
The 25 scholars most cited alongside Kyle Bystrom, 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 | Matminer: An open source toolkit for materials data mining Hit paper breakdown → | 2018 | 713 |
| 2 | 2023 | 36 | |
| 3 | 2024 | 24 | |
| 4 | 2024 | 22 | |
| 5 | 2024 | 7 | |
| 6 | 2024 | 7 |
About Kyle Bystrom
Kyle Bystrom is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Catalysis and Radiation, having authored 6 papers that have together received 809 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (6 papers), X-ray Diffraction in Crystallography (3 papers), Computational Drug Discovery Methods (2 papers), Nuclear Physics and Applications (1 paper), Catalysis and Oxidation Reactions (1 paper), Ionic liquids properties and applications (1 paper), Surface and Thin Film Phenomena (1 paper) and Electrochemical Analysis and Applications (1 paper). The work is most often cited by research in Materials Chemistry (684 citations), Computational Theory and Mathematics (127 citations), Metals and Alloys (19 citations), Catalysis (50 citations) and Surfaces, Coatings and Films (25 citations). Kyle Bystrom has collaborated with scholars based in United States, Norway and Belgium. Frequent co-authors include Mark Asta, Kristin A. Persson, Alireza Faghaninia, Maxwell Dylla, Ian Foster, Nils Zimmermann, Qi Wang, Saurabh Bajaj, Anubhav Jain and Alexander Dunn. Their work appears in journals such as npj Computational Materials, Computational Materials Science, Physical review. B., The Journal of Physical Chemistry Letters and Journal of Chemical Theory and Computation.
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.