Kyle Mills
Impact in
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- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Materials Science 7
- Block Copolymer Self-Assembly 1
- Material Dynamics and Properties 1
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- Quantum many-body systems 2
- Co-authors
- Isaac Tamblyn (9 shared papers)Michael Spanner (2 shared papers)Kevin Ryczko (3 shared papers)Lei Wang (1 shared paper)Robert H. Morris (1 shared paper)Rafael C. Carvalho (1 shared paper)Sarah M. Hamylton (1 shared paper)Christa M. Homenick (1 shared paper)
In The Last Decade
Kyle Mills
12 papers receiving 393 citations
Peers
Comparison fields: 5 of 103
- Materials Chemistry 156
- Computational Theory and Mathematics 51
- Atomic and Molecular Physics, and Optics 84
- Ecological Modeling 11
- Applied Microbiology and Biotechnology 5
Countries citing papers authored by Kyle Mills
This map shows the geographic impact of Kyle Mills'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 Mills with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyle Mills more than expected).
Fields of papers citing papers by Kyle Mills
This network shows the impact of papers produced by Kyle Mills. 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 Mills. The network helps show where Kyle Mills may publish in the future.
Co-authors
The 13 scholars most cited alongside Kyle Mills, 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 | 2017 | 119 | |
| 2 | 2020 | 78 | |
| 3 | 2019 | 39 | |
| 4 | 2020 | 36 | |
| 5 | 2018 | 33 | |
| 6 | Medical management of stable coronary artery disease. | 2011 | 23 |
| 7 | Treatment of nursing home-acquired pneumonia. | 2009 | 23 |
| 8 | 2020 | 18 | |
| 9 | 2018 | 17 | |
| 10 | 2020 | 9 | |
| 11 | 2018 | 2 | |
| 12 | Adversarial Generation of Mesoscale Surfaces from Small-Scale Chemical Motifs | 2020 | 1 |
About Kyle Mills
Kyle Mills is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics, Artificial Intelligence, Electrical and Electronic Engineering and Surgery, having authored 12 papers that have together received 398 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Quantum many-body systems (2 papers), Species Distribution and Climate Change (1 paper), Block Copolymer Self-Assembly (1 paper), Quantum Computing Algorithms and Architecture (1 paper), Material Dynamics and Properties (1 paper), Catalysis and Oxidation Reactions (1 paper) and Remote Sensing and LiDAR Applications (1 paper). The work is most often cited by research in Materials Chemistry (156 citations), Computational Theory and Mathematics (51 citations), Atomic and Molecular Physics, and Optics (84 citations), Ecological Modeling (11 citations) and Applied Microbiology and Biotechnology (5 citations). Kyle Mills has collaborated with scholars based in Canada, Belgium and Australia. Frequent co-authors include Isaac Tamblyn, Michael Spanner, Kevin Ryczko, Lei Wang, Robert H. Morris, Rafael C. Carvalho, Sarah M. Hamylton, Christa M. Homenick, Ira Dauber and Edward H. Sargent. Their work appears in journals such as Physical review. A, International Journal of Applied Earth Observation and Geoinformation, The Journal of Physical Chemistry, Chemical Science and Matter.
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.