Riley J. Hickman
- Materials Chemistry top 10%
- Biomedical Engineering
- Computational Theory and Mathematics top 5%
- Electrical and Electronic Engineering
- Molecular Biology
- Co-authors
- Alán Aspuru‐GuzikMatteo AldeghiChristine AllenPauric BanniganZeqing BaoFlorian HäseCyrille LavigneCher Tian Ser
- Topics
- Innovative Microfluidic and Catalytic Techniques Innovation (7 papers)Computational Drug Discovery Methods (7 papers)Machine Learning in Materials Science (6 papers)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Riley J. Hickman
21 papers receiving 807 citations
Hit Papers
Peers
Comparison fields: 5 of 112
- Materials Chemistry 386
- Biomedical Engineering 190
- Computational Theory and Mathematics 172
- Electrical and Electronic Engineering 116
- Molecular Biology 102
Countries citing papers authored by Riley J. Hickman
This map shows the geographic impact of Riley J. Hickman'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 Riley J. Hickman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Riley J. Hickman more than expected).
Fields of papers citing papers by Riley J. Hickman
This network shows the impact of papers produced by Riley J. Hickman. 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 Riley J. Hickman. The network helps show where Riley J. Hickman may publish in the future.
Co-authorship network of co-authors of Riley J. Hickman
This figure shows the co-authorship network connecting the top 25 collaborators of Riley J. Hickman. A scholar is included among the top collaborators of Riley J. Hickman 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 Riley J. Hickman. Riley J. Hickman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 13 | |
| 2 | 25 | |
| 3 | 3 | |
| 4 | 10 | |
| 5 | 41 | |
| 6 | 2 | |
| 7 | 23 | |
| 8 | 48 | |
| 9 | Machine learning models to accelerate the design of polymeric long-acting injectablesbreakdown → | 127 |
| 10 | 62 | |
| 11 | 17 | |
| 12 | 12 | |
| 13 | 13 | |
| 14 | 56 | |
| 15 | 9 | |
| 16 | Data-Driven Strategies for Accelerated Materials Designbreakdown → | 299 |
| 17 | 19 | |
| 18 | 7 | |
| 19 | 15 | |
| 20 | 20 |
About Riley J. Hickman
Riley J. Hickman is a scholar working on Computational Theory and Mathematics, Pharmaceutical Science and Spectroscopy, having authored 21 papers that have together received 827 indexed citations. Recurring topics across this work include Innovative Microfluidic and Catalytic Techniques Innovation (7 papers), Computational Drug Discovery Methods (7 papers) and Machine Learning in Materials Science (6 papers). The work is most often cited by research in Computational Theory and Mathematics (172 citations), Materials Chemistry (386 citations) and Pharmaceutical Science (43 citations). Riley J. Hickman has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Alán Aspuru‐Guzik, Matteo Aldeghi, Christine Allen, Pauric Bannigan, Zeqing Bao, Florian Häse, Cyrille Lavigne, Cher Tian Ser, Mario Krenn and Zhenpeng Yao. Their work appears in journals such as Nature Communications, Accounts of Chemical Research and Advanced Drug Delivery Reviews.
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.