Ryan Murdock

1.1k total citations · 1 hit paper
8 papers, 694 citations indexed

About

Ryan Murdock is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Social Psychology. According to data from OpenAlex, Ryan Murdock has authored 8 papers receiving a total of 694 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computational Theory and Mathematics, 5 papers in Materials Chemistry and 2 papers in Social Psychology. Recurrent topics in Ryan Murdock's work include Machine Learning in Materials Science (5 papers), Computational Drug Discovery Methods (5 papers) and Human-Automation Interaction and Safety (2 papers). Ryan Murdock is often cited by papers focused on Machine Learning in Materials Science (5 papers), Computational Drug Discovery Methods (5 papers) and Human-Automation Interaction and Safety (2 papers). Ryan Murdock collaborates with scholars based in United States and Germany. Ryan Murdock's co-authors include Taylor D. Sparks, Steven K. Kauwe, Anthony Wang, Jakoah Brgoch, Anton O. Oliynyk, Aleksander Gurlo, Kristin A. Persson, Jake Graser, Jeffrey Yao and Matthew Christian and has published in prestigious journals such as Chemistry of Materials, Psychophysiology and The Journal Of Hand Surgery.

In The Last Decade

Ryan Murdock

8 papers receiving 672 citations

Hit Papers

Machine Learning for Materials Scientists: An Introductor... 2020 2026 2022 2024 2020 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
    2020 323 citations Anthony Wang, Ryan Murdock et al. Chemistry of Materials profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ryan Murdock United States 6 493 121 115 89 74 8 694
Steven K. Kauwe United States 11 743 1.5× 185 1.5× 156 1.4× 153 1.7× 107 1.4× 15 970
Nicholas Wagner United States 8 359 0.7× 34 0.3× 146 1.3× 60 0.7× 267 3.6× 9 659
Rolf Merz Germany 15 256 0.5× 34 0.3× 81 0.7× 205 2.3× 74 1.0× 52 626
Kangming Li China 15 309 0.6× 24 0.2× 195 1.7× 143 1.6× 110 1.5× 46 654
Anthony Wang Germany 5 425 0.9× 95 0.8× 109 0.9× 82 0.9× 66 0.9× 5 554
Daylond Hooper United States 7 382 0.8× 68 0.6× 104 0.9× 98 1.1× 130 1.8× 13 581
Zhenlin Zhang China 14 285 0.6× 9 0.1× 111 1.0× 97 1.1× 235 3.2× 41 652
Yusuke Noda Japan 17 293 0.6× 13 0.1× 321 2.8× 50 0.6× 38 0.5× 70 820

Countries citing papers authored by Ryan Murdock

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Ryan Murdock

This figure shows the co-authorship network connecting the top 25 collaborators of Ryan Murdock. A scholar is included among the top collaborators of Ryan Murdock based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ryan Murdock. Ryan Murdock is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
Baird, Sterling G., et al.. (2023). Generative adversarial networks and diffusion models in material discovery. Digital Discovery. 3(1). 62–80. 46 indexed citations
2.
3.
Geuss, Michael N., et al.. (2022). Unintended Consequences of Trying to Help: Augmented Target Recognition Cues Bias Perception. Journal of Vision. 22(14). 3362–3362. 1 indexed citations
4.
Wang, Anthony, Steven K. Kauwe, Ryan Murdock, & Taylor D. Sparks. (2021). Compositionally restricted attention-based network for materials property predictions. npj Computational Materials. 7(1). 140 indexed citations
5.
Wang, Anthony, Ryan Murdock, Steven K. Kauwe, et al.. (2020). Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices. Chemistry of Materials. 32(12). 4954–4965. 323 indexed citations breakdown →
6.
Murdock, Ryan, Steven K. Kauwe, Anthony Wang, & Taylor D. Sparks. (2020). Is Domain Knowledge Necessary for Machine Learning Materials Properties?. Integrating materials and manufacturing innovation. 9(3). 221–227. 56 indexed citations
7.
Kauwe, Steven K., Jake Graser, Ryan Murdock, & Taylor D. Sparks. (2019). Can machine learning find extraordinary materials?. Computational Materials Science. 174. 109498–109498. 80 indexed citations
8.
Yao, Jeffrey, Dan A. Zlotolow, Ryan Murdock, & Matthew Christian. (2010). Suture Button Compared With K-Wire Fixation for Maintenance of Posttrapeziectomy Space Height in a Cadaver Model of Lateral Pinch. The Journal Of Hand Surgery. 35(12). 2061–2065. 45 indexed citations

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.

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