Christopher Kuenneth
Impact in
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- Machine Learning in Materials Science
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Materials Science 9
- MXene and MAX Phase Materials 1
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- Computational Drug Discovery Methods 4
- Co-authors
- Rampi Ramprasad (7 shared papers)Rishi Gurnani (2 shared papers)Babetta L. Marrone (1 shared paper)Ghanshyam Pilania (1 shared paper)Carl N. Iverson (1 shared paper)Yang Cao (1 shared paper)Tran Doan Huan (1 shared paper)Gregory Allen Sotzing (1 shared paper)
- Journals
- Nature Communications (2 papers)Macromolecules (1 paper)physica status solidi (RRL) - Rapid Research Letters (1 paper)The Journal of Physical Chemistry A (1 paper)Communications Materials (1 paper)
- Partner nations
- United StatesGermanyBelgium
In The Last Decade
Christopher Kuenneth
10 papers receiving 501 citations
Christopher Kuenneth's Hit Papers
Peers
Comparison fields: 5 of 71
- Materials Chemistry 332
- Computational Theory and Mathematics 103
- Polymers and Plastics 69
- Biomedical Engineering 94
- Environmental Chemistry 21
Countries citing papers authored by Christopher Kuenneth
This map shows the geographic impact of Christopher Kuenneth'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 Christopher Kuenneth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher Kuenneth more than expected).
Fields of papers citing papers by Christopher Kuenneth
This network shows the impact of papers produced by Christopher Kuenneth. 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 Christopher Kuenneth. The network helps show where Christopher Kuenneth may publish in the future.
Co-authors
The 19 scholars most cited alongside Christopher Kuenneth, 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 | polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics Hit paper breakdown → | 2023 | 178 |
| 2 | 2021 | 80 | |
| 3 | 2023 | 79 | |
| 4 | 2024 | 63 | |
| 5 | 2022 | 50 | |
| 6 | 2020 | 19 | |
| 7 | 2022 | 19 | |
| 8 | 2023 | 13 | |
| 9 | 2025 | 6 | |
| 10 | 2023 | 6 | |
| 11 | 2026 | 0 |
About Christopher Kuenneth
Christopher Kuenneth is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Environmental Chemistry, Biomaterials and Catalysis, having authored 11 papers that have together received 513 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (9 papers), Computational Drug Discovery Methods (4 papers), Chemistry and Chemical Engineering (2 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Advanced Polymer Synthesis and Characterization (2 papers), Nanoparticle-Based Drug Delivery (1 paper), Catalysis and Oxidation Reactions (1 paper) and MXene and MAX Phase Materials (1 paper). The work is most often cited by research in Materials Chemistry (332 citations), Computational Theory and Mathematics (103 citations), Polymers and Plastics (69 citations), Biomedical Engineering (94 citations) and Environmental Chemistry (21 citations). Christopher Kuenneth has collaborated with scholars based in United States, Germany and Belgium. Frequent co-authors include Rampi Ramprasad, Rishi Gurnani, Babetta L. Marrone, Ghanshyam Pilania, Carl N. Iverson, Yang Cao, Tran Doan Huan, Gregory Allen Sotzing, Ajinkya A. Deshmukh and Stuti Shukla. Their work appears in journals such as Nature Communications, Macromolecules, physica status solidi (RRL) - Rapid Research Letters, The Journal of Physical Chemistry A and Communications Materials.
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.