Riley J. Hickman

1.5k total citations · 2 hit papers
21 papers, 827 citations indexed

About

Riley J. Hickman is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Biomedical Engineering. According to data from OpenAlex, Riley J. Hickman has authored 21 papers receiving a total of 827 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computational Theory and Mathematics, 8 papers in Materials Chemistry and 7 papers in Biomedical Engineering. Recurrent topics in Riley J. Hickman's work include Innovative Microfluidic and Catalytic Techniques Innovation (7 papers), Computational Drug Discovery Methods (7 papers) and Machine Learning in Materials Science (6 papers). Riley J. Hickman is often cited by papers focused on Innovative Microfluidic and Catalytic Techniques Innovation (7 papers), Computational Drug Discovery Methods (7 papers) and Machine Learning in Materials Science (6 papers). Riley J. Hickman collaborates with scholars based in Canada, United States and United Kingdom. Riley J. Hickman's co-authors include Alán Aspuru‐Guzik, Matteo Aldeghi, Pauric Bannigan, Christine Allen, Zeqing Bao, Florian Häse, Cyrille Lavigne, AkshatKumar Nigam, Cher Tian Ser and Zhenpeng Yao and has published in prestigious journals such as Nature Communications, Accounts of Chemical Research and Advanced Drug Delivery Reviews.

In The Last Decade

Riley J. Hickman

21 papers receiving 807 citations

Hit Papers

align trajectories

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.

Data-Driven Strategies for Accelerated Materials Design

2021 299 citations Robert Pollice, Gabriel dos Passos Gomes et al. Accounts of Chemical Research profile →
Machine learning models to accelerate the design of polymeric long-acting injectables

2023 127 citations Pauric Bannigan, Zeqing Bao et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Riley J. Hickman Canada	14	386	190	172	116	102	21	827
S. Hessam M. Mehr United Kingdom	12	289 0.7×	184 1.0×	86 0.5×	49 0.4×	105 1.0×	22	566
Kobi Felton United Kingdom	9	287 0.7×	275 1.4×	106 0.6×	100 0.9×	67 0.7×	12	607
Connor J. Taylor United Kingdom	12	312 0.8×	464 2.4×	136 0.8×	41 0.4×	119 1.2×	16	864
Stefan Glatzel United Kingdom	13	530 1.4×	494 2.6×	97 0.6×	184 1.6×	138 1.4×	20	1.3k
Yeonjoon Kim South Korea	21	532 1.4×	287 1.5×	223 1.3×	96 0.8×	210 2.1×	60	1.4k
Wan Zheng China	20	261 0.7×	133 0.7×	67 0.4×	118 1.0×	148 1.5×	78	1.1k
Jonathan N. Jaworski United States	11	458 1.2×	324 1.7×	220 1.3×	77 0.7×	352 3.5×	12	1.2k
Nicola Rankin United Kingdom	5	594 1.5×	309 1.6×	141 0.8×	183 1.6×	109 1.1×	5	1.1k
Vincenza Dragone United Kingdom	6	485 1.3×	934 4.9×	187 1.1×	187 1.6×	193 1.9×	8	1.5k
Shibing Wang China	19	548 1.4×	63 0.3×	36 0.2×	185 1.6×	70 0.7×	54	1.1k

Countries citing papers authored by Riley J. Hickman

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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20 of 20 papers shown

Hickman, Riley J., Malcolm Sim, Sergio Pablo‐García, et al.. (2025). Atlas: a brain for self-driving laboratories. Digital Discovery. 4(4). 1006–1029. 13 indexed citations

Wu, Tianyi, Sina Kheiri, Riley J. Hickman, et al.. (2025). Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties. Nature Communications. 16(1). 1473–1473. 25 indexed citations

Yakavets, Ilya, Sina Kheiri, Jennifer Cruickshank, et al.. (2025). Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays. Science Advances. 11(31). eadt1851–eadt1851. 3 indexed citations

Bannigan, Pauric, Riley J. Hickman, Alán Aspuru‐Guzik, & Christine Allen. (2024). The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?. Advanced Healthcare Materials. 13(29). e2401312–e2401312. 10 indexed citations

Sim, Malcolm, Mohammad Ghazi Vakili, Felix Strieth‐Kalthoff, et al.. (2024). ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories. Matter. 7(9). 2959–2977. 41 indexed citations

Hickman, Riley J., Pauric Bannigan, Zeqing Bao, Alán Aspuru‐Guzik, & Christine Allen. (2023). Self-driving laboratories: A paradigm shift in nanomedicine development. Matter. 6(4). 1071–1081. 48 indexed citations

Cavell, Andrew C., Christopher J. Forman, Si Yue Guo, et al.. (2023). The Role of Experimental Noise in a Hybrid Classical-Molecular Computer to Solve Combinatorial Optimization Problems. ACS Central Science. 9(7). 1453–1465. 2 indexed citations

Tom, Gary, et al.. (2023). Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS. Digital Discovery. 2(3). 759–774. 23 indexed citations

Bannigan, Pauric, Zeqing Bao, Riley J. Hickman, et al.. (2023). Machine learning models to accelerate the design of polymeric long-acting injectables. Nature Communications. 14(1). 35–35. 127 indexed citations breakdown →

10.

Bao, Zeqing, et al.. (2023). Revolutionizing drug formulation development: The increasing impact of machine learning. Advanced Drug Delivery Reviews. 202. 115108–115108. 62 indexed citations

11.

Bao, Zeqing, et al.. (2023). Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug. Drug Delivery and Translational Research. 14(7). 1872–1887. 17 indexed citations

12.

Hickman, Riley J., et al.. (2023). Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization. Reaction Chemistry & Engineering. 8(9). 2284–2296. 12 indexed citations

13.

Hickman, Riley J., Matteo Aldeghi, Florian Häse, & Alán Aspuru-Guzik. (2022). Bayesian optimization with known experimental and design constraints for chemistry applications. Digital Discovery. 1(5). 732–744. 56 indexed citations

14.

Seifrid, Martin, Riley J. Hickman, Andrés Aguilar‐Granda, et al.. (2022). Routescore: Punching the Ticket to More Efficient Materials Development. ACS Central Science. 8(1). 122–131. 13 indexed citations

15.

Guo, Si Yue, Pascal Friederich, Yudong Cao, et al.. (2021). A molecular computing approach to solving optimization problems via programmable microdroplet arrays. Matter. 4(4). 1107–1124. 9 indexed citations

16.

Pollice, Robert, Gabriel dos Passos Gomes, Matteo Aldeghi, et al.. (2021). Data-Driven Strategies for Accelerated Materials Design. Accounts of Chemical Research. 54(4). 849–860. 299 indexed citations breakdown →

17.

Cavell, Andrew C., Guoping Li, Abhishek Sharma, et al.. (2020). Optical monitoring of polymerizations in droplets with high temporal dynamic range. Chemical Science. 11(10). 2647–2656. 19 indexed citations

18.

Lang, Robert A., Riley J. Hickman, & Tao Zeng. (2019). VHEGEN: A vibronic Hamiltonian expansion generator for trigonal and tetragonal polyatomic systems. Computer Physics Communications. 247. 106946–106946. 7 indexed citations

19.

Hickman, Riley J., Robert A. Lang, & Tao Zeng. (2018). General formalism for vibronic Hamiltonians in tetragonal symmetry and beyond. Physical Chemistry Chemical Physics. 20(17). 12312–12322. 15 indexed citations

20.

Zeng, Tao, et al.. (2017). General Formalism of Vibronic Hamiltonians for Tetrahedral and Octahedral Systems: Problems That Involve T, E States and t, e Vibrations. Journal of Chemical Theory and Computation. 13(10). 5004–5018. 20 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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