Riley J. Hickman
Riley J. Hickman
Co-founder and Director of R&D at Intrepid Labs Inc, University of Toronto
Verified email at - Homepage
Cited by
Cited by
Data-driven strategies for accelerated materials design
R Pollice, G dos Passos Gomes, M Aldeghi, RJ Hickman, M Krenn, ...
Accounts of Chemical Research 54 (4), 849-860, 2021
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
F Häse, M Aldeghi, RJ Hickman, LM Roch, A Aspuru-Guzik
Applied Physics Reviews 8 (3), 2021
Olympus: a benchmarking framework for noisy optimization and experiment planning
F Häse, M Aldeghi, RJ Hickman, LM Roch, M Christensen, E Liles, ...
Machine Learning: Science and Technology 2 (3), 035021, 2021
Machine learning models to accelerate the design of polymeric long-acting injectables
P Bannigan, Z Bao, RJ Hickman, M Aldeghi, F Häse, A Aspuru-Guzik, ...
Nature communications 14 (1), 35, 2023
Assigning confidence to molecular property prediction
AK Nigam, R Pollice, MFD Hurley, RJ Hickman, M Aldeghi, N Yoshikawa, ...
Expert opinion on drug discovery 16 (9), 1009-1023, 2021
Bayesian optimization with known experimental and design constraints for chemistry applications
RJ Hickman, M Aldeghi, F Häse, A Aspuru-Guzik
Digital Discovery 1 (5), 732-744, 2022
Golem: an algorithm for robust experiment and process optimization
M Aldeghi, F Häse, RJ Hickman, I Tamblyn, A Aspuru-Guzik
Chemical Science 12 (44), 14792-14807, 2021
Self-driving laboratories: A paradigm shift in nanomedicine development
RJ Hickman, P Bannigan, Z Bao, A Aspuru-Guzik, C Allen
Matter 6 (4), 1071-1081, 2023
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
G Tom, RJ Hickman, A Zinzuwadia, A Mohajeri, B Sanchez-Lengeling, ...
Digital Discovery 2 (3), 759-774, 2023
General Formalism of Vibronic Hamiltonians for Tetrahedral and Octahedral Systems: Problems That Involve T, E States and t, e Vibrations
T Zeng, RJ Hickman, A Kadri, I Seidu
Journal of Chemical Theory and Computation 13 (10), 5004-5018, 2017
Optical monitoring of polymerizations in droplets with high temporal dynamic range
AC Cavell, VK Krasecki, G Li, A Sharma, H Sun, MP Thompson, ...
Chemical science 11 (10), 2647-2656, 2020
Revolutionizing drug formulation development: the increasing impact of machine learning
Z Bao, J Bufton, RJ Hickman, A Aspuru-Guzik, P Bannigan, C Allen
Advanced Drug Delivery Reviews, 115108, 2023
Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization
RJ Hickman, J Ruža, H Tribukait, LM Roch, A García-Durán
Reaction Chemistry & Engineering 8 (9), 2284-2296, 2023
Routescore: punching the ticket to more efficient materials development
M Seifrid, RJ Hickman, A Aguilar-Granda, C Lavigne, J Vestfrid, TC Wu, ...
ACS Central Science 8 (1), 122-131, 2022
General formalism for vibronic Hamiltonians in tetragonal symmetry and beyond
RJ Hickman, RA Lang, T Zeng
Physical Chemistry Chemical Physics 20 (17), 12312-12322, 2018
Delocalized, asynchronous, closed-loop discovery of organic laser emitters
F Strieth-Kalthoff, H Hao, V Rathore, J Derasp, T Gaudin, NH Angello, ...
Science 384 (6697), eadk9227, 2024
A molecular computing approach to solving optimization problems via programmable microdroplet arrays
SY Guo, P Friederich, Y Cao, TC Wu, CJ Forman, D Mendoza, ...
Matter 4 (4), 1107-1124, 2021
ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories
M Sim, MG Vakili, F Strieth-Kalthoff, H Hao, RJ Hickman, S Miret, ...
Matter, 2023
Atlas: a brain for self-driving laboratories
R Hickman, M Sim, S Pablo-García, I Woolhouse, H Hao, Z Bao, ...
Gemini: Dynamic bias correction for autonomous experimentation and molecular simulation
RJ Hickman, F Häse, LM Roch, A Aspuru-Guzik
arXiv preprint arXiv:2103.03391, 2021
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