Ryan Murdock
Impact in
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
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- Computational Drug Discovery Methods
Papers in
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- Computational Drug Discovery Methods 5
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- Machine Learning in Materials Science 5
- X-ray Diffraction in Crystallography 2
- Co-authors
- Taylor D. Sparks (5 shared papers)Steven K. Kauwe (4 shared papers)Anthony Wang (3 shared papers)Kristin A. Persson (1 shared paper)Aleksander Gurlo (1 shared paper)Anton O. Oliynyk (1 shared paper)Jakoah Brgoch (1 shared paper)Jake Graser (1 shared paper)
- Journals
- npj Computational Materials (1 paper)Chemistry of Materials (1 paper)Journal of Vision (1 paper)The Journal Of Hand Surgery (1 paper)Integrating materials and manufacturing innovation (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Ryan Murdock
8 papers receiving 672 citations
Ryan Murdock's Hit Papers
Peers
Comparison fields: 5 of 83
- Materials Chemistry 493
- Computational Theory and Mathematics 121
- Catalysis 40
- Metals and Alloys 14
- Rehabilitation 35
Countries citing papers authored by Ryan Murdock
This map shows the geographic impact of Ryan Murdock'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 Ryan Murdock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan Murdock more than expected).
Fields of papers citing papers by Ryan Murdock
This network shows the impact of papers produced by Ryan Murdock. 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 Ryan Murdock. The network helps show where Ryan Murdock may publish in the future.
Co-authors
The 19 scholars most cited alongside Ryan Murdock, 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 | Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices Hit paper breakdown → | 2020 | 323 |
| 2 | 2021 | 140 | |
| 3 | 2019 | 80 | |
| 4 | 2020 | 56 | |
| 5 | 2023 | 46 | |
| 6 | 2010 | 45 | |
| 7 | 2023 | 3 | |
| 8 | 2022 | 1 |
About Ryan Murdock
Ryan Murdock is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Social Psychology, Surgery and Rehabilitation, having authored 8 papers that have together received 694 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (5 papers), Machine Learning in Materials Science (5 papers), Human-Automation Interaction and Safety (2 papers), X-ray Diffraction in Crystallography (2 papers), Neural and Behavioral Psychology Studies (1 paper), Cell Image Analysis Techniques (1 paper), Elbow and Forearm Trauma Treatment (1 paper) and Catalysis and Oxidation Reactions (1 paper). The work is most often cited by research in Materials Chemistry (493 citations), Computational Theory and Mathematics (121 citations), Catalysis (40 citations), Metals and Alloys (14 citations) and Rehabilitation (35 citations). Ryan Murdock has collaborated with scholars based in United States and Germany. Frequent co-authors include Taylor D. Sparks, Steven K. Kauwe, Anthony Wang, Kristin A. Persson, Aleksander Gurlo, Anton O. Oliynyk, Jakoah Brgoch, Jake Graser, Jeffrey Yao and Matthew Christian. Their work appears in journals such as npj Computational Materials, Chemistry of Materials, Journal of Vision, The Journal Of Hand Surgery and Integrating materials and manufacturing innovation.
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.