Taylor D. Sparks
- Materials Chemistry top 1%
- Electrical and Electronic Engineering top 5%
- Mechanical Engineering top 5%
- Electronic, Optical and Magnetic Materials top 5%
- Computational Theory and Mathematics top 1%
- Co-authors
- Steven K. KauweMichael W. GaultoisAnton O. OliynykDavid R. ClarkeRyan MurdockRam SeshadriJake GraserLeila Ghadbeigi
- Topics
- Machine Learning in Materials Science (41 papers)Computational Drug Discovery Methods (15 papers)X-ray Diffraction in Crystallography (12 papers)
- Cited by
- Materials ChemistryComputational Theory and MathematicsElectronic, Optical and Magnetic Materials
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Taylor D. Sparks
105 papers receiving 3.9k citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Materials Chemistry 3.0k
- Electrical and Electronic Engineering 1.1k
- Mechanical Engineering 512
- Electronic, Optical and Magnetic Materials 455
- Computational Theory and Mathematics 406
Countries citing papers authored by Taylor D. Sparks
This map shows the geographic impact of Taylor D. Sparks'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 Taylor D. Sparks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taylor D. Sparks more than expected).
Fields of papers citing papers by Taylor D. Sparks
This network shows the impact of papers produced by Taylor D. Sparks. 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 Taylor D. Sparks. The network helps show where Taylor D. Sparks may publish in the future.
Co-authorship network of co-authors of Taylor D. Sparks
This figure shows the co-authorship network connecting the top 25 collaborators of Taylor D. Sparks. A scholar is included among the top collaborators of Taylor D. Sparks 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 Taylor D. Sparks. Taylor D. Sparks is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 1 | |
| 4 | 7 | |
| 5 | 46 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 3 | |
| 10 | 19 | |
| 11 | 9 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 12 | |
| 15 | 5 | |
| 16 | 140 | |
| 17 | Machine Learning for Materials Scientists: An Introductory Guide toward Best Practicesbreakdown → | 323 |
| 18 | 33 | |
| 19 | 1 | |
| 20 | 9 |
About Taylor D. Sparks
Taylor D. Sparks is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Acoustics and Ultrasonics, having authored 112 papers that have together received 4.0k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (41 papers), Computational Drug Discovery Methods (15 papers) and X-ray Diffraction in Crystallography (12 papers). The work is most often cited by research in Materials Chemistry (3.0k citations), Computational Theory and Mathematics (406 citations) and Electronic, Optical and Magnetic Materials (455 citations). Taylor D. Sparks has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Steven K. Kauwe, Michael W. Gaultois, Anton O. Oliynyk, David R. Clarke, Ryan Murdock, Ram Seshadri, Jake Graser, Leila Ghadbeigi, Jakoah Brgoch and Anthony Wang. Their work appears in journals such as Journal of the American Chemical Society, Physical Review Letters and Nature Communications.
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.