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 | 255 | 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 | 122 | 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 | 77 | 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 | 54 | 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 | 47 | 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 | 37 | 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 | 27 | 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 | 22 | 2023 |
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 | 20 | 2020 |
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 | 19 | 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 | 19 | 2017 |
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 | 17 | 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 | 17 | 2022 |
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 | 16 | 2023 |
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 | 15 | 2018 |
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 | 13 | 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 | 10 | 2023 |
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 | 8 | 2024 |
Atlas: a brain for self-driving laboratories R Hickman, M Sim, S Pablo-García, I Woolhouse, H Hao, Z Bao, ... | 8 | 2023 |
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 | 6 | 2021 |