Christopher Kuenneth
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
Papers in
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- Machine Learning in Materials Science 8
- Electronic and Structural Properties of Oxides 1
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- Computational Drug Discovery Methods 5
- Co-authors
- Rampi Ramprasad (8 shared papers)Rishi Gurnani (2 shared papers)Carl N. Iverson (1 shared paper)Babetta L. Marrone (1 shared paper)Ghanshyam Pilania (1 shared paper)Beatriz G. del Rio (1 shared paper)Tran Doan Huan (1 shared paper)Alfred Kersch (1 shared paper)
- Journals
- Macromolecules (2 papers)Nature Communications (2 papers)Chemistry of Materials (1 paper)The Journal of Physical Chemistry A (1 paper)Communications Materials (1 paper)
- Partner nations
- United StatesGermanyChina
In The Last Decade
Christopher Kuenneth
10 papers receiving 378 citations
Christopher Kuenneth's Hit Papers
Peers
Comparison fields: 5 of 66
- Computational Theory and Mathematics 101
- Materials Chemistry 275
- Polymers and Plastics 55
- Environmental Chemistry 21
- Biomedical Engineering 79
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 18 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 | 129 |
| 2 | 2021 | 72 | |
| 3 | 2023 | 53 | |
| 4 | 2024 | 45 | |
| 5 | 2022 | 35 | |
| 6 | 2020 | 18 | |
| 7 | 2022 | 16 | |
| 8 | 2023 | 12 | |
| 9 | 2023 | 4 | |
| 10 | 2021 | 3 |
About Christopher Kuenneth
Christopher Kuenneth is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Molecular Biology, Organic Chemistry and Biomaterials, having authored 10 papers that have together received 387 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (5 papers), Chemistry and Chemical Engineering (2 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Advanced Polymer Synthesis and Characterization (2 papers), Dielectric materials and actuators (1 paper), Catalysis and Oxidation Reactions (1 paper) and Electronic and Structural Properties of Oxides (1 paper). The work is most often cited by research in Computational Theory and Mathematics (101 citations), Materials Chemistry (275 citations), Polymers and Plastics (55 citations), Environmental Chemistry (21 citations) and Biomedical Engineering (79 citations). Christopher Kuenneth has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Rampi Ramprasad, Rishi Gurnani, Carl N. Iverson, Babetta L. Marrone, Ghanshyam Pilania, Beatriz G. del Rio, Tran Doan Huan, Alfred Kersch, Yang Cao and Ajinkya A. Deshmukh. Their work appears in journals such as Macromolecules, Nature Communications, Chemistry of Materials, 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.