Matthew K. Horton
- Materials Chemistry top 5%
- Machine Learning in Materials Science 28
- X-ray Diffraction in Crystallography 16
- Electronic and Structural Properties of Oxides 9
- Condensed Matter Physics top 5%
- GaN-based semiconductor devices and materials 10
- Advanced Condensed Matter Physics 4
- Catalysis top 5%
- Structural Biology top 10%
- Metals and Alloys top 10%
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- Metal and Thin Film Mechanics 10
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- Semiconductor materials and devices 9
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- Magnetic and transport properties of perovskites and related materials 3
- Co-authors
- Kristin A. PerssonShyam DwaraknathAnubhav JainShyue Ping OngJoseph H. MontoyaJason M. MunroPatrick HuckMiao Liu
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Matthew K. Horton
51 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 88
- Materials Chemistry 1.5k
- Condensed Matter Physics 330
- Catalysis 159
- Structural Biology 28
- Metals and Alloys 39
Countries citing papers authored by Matthew K. Horton
This map shows the geographic impact of Matthew K. Horton'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 Matthew K. Horton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew K. Horton more than expected).
Fields of papers citing papers by Matthew K. Horton
This network shows the impact of papers produced by Matthew K. Horton. 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 Matthew K. Horton. The network helps show where Matthew K. Horton may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Matthew K. Horton, 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 | 2025 | 1 | |
| 2 | 2024 | 18 | |
| 3 | 2023 | 15 | |
| 4 | 2023 | 8 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 30 | |
| 7 | 2022 | 68 | |
| 8 | 2022 | 43 | |
| 9 | 2022 | 15 | |
| 10 | 2022 | 12 | |
| 11 | 2021 | 17 | |
| 12 | 2021 | 36 | |
| 13 | 2021 | 31 | |
| 14 | 2021 | 67 | |
| 15 | 2020 | 32 | |
| 16 | 2020 | 38 | |
| 17 | 2019 | 10 | |
| 18 | 2019 | 107 | |
| 19 | 2017 | 259 | |
| 20 | 2013 | 54 |
About Matthew K. Horton
Matthew K. Horton is a scholar working on Condensed Matter Physics, Materials Chemistry and Catalysis, having authored 51 papers that have together received 2.1k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (28 papers), X-ray Diffraction in Crystallography (16 papers), Metal and Thin Film Mechanics (10 papers), GaN-based semiconductor devices and materials (10 papers), Electronic and Structural Properties of Oxides (9 papers), Semiconductor materials and devices (9 papers), Advanced Condensed Matter Physics (4 papers) and Magnetic and transport properties of perovskites and related materials (3 papers). The work is most often cited by research in Materials Chemistry (1.5k citations), Condensed Matter Physics (330 citations) and Catalysis (159 citations). Matthew K. Horton has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Kristin A. Persson, Shyam Dwaraknath, Anubhav Jain, Shyue Ping Ong, Joseph H. Montoya, Jason M. Munro, Patrick Huck, Miao Liu, C. J. Humphreys and M. A. Moram. Their work appears in journals such as Physical Review Letters, Nano Letters and ACS Nano.
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.