| Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation M Krenn, F Häse, AK Nigam, P Friederich, A Aspuru-Guzik Machine Learning: Science and Technology 1 (4), 045024, 2020 | 558* | 2020 |
| Self-driving laboratory for accelerated discovery of thin-film materials BP MacLeod, FGL Parlane, TD Morrissey, F Häse, LM Roch, ... Science Advances 6 (20), eaaz8867, 2020 | 387 | 2020 |
| Phoenics: a Bayesian optimizer for chemistry F Hase, LM Roch, C Kreisbeck, A Aspuru-Guzik ACS central science 4 (9), 1134-1145, 2018 | 269 | 2018 |
| Machine-learned potentials for next-generation matter simulations P Friederich, F Häse, J Proppe, A Aspuru-Guzik Nature Materials 20 (6), 750-761, 2021 | 230 | 2021 |
| Next-generation experimentation with self-driving laboratories F Häse, LM Roch, A Aspuru-Guzik Trends in Chemistry 1 (3), 282-291, 2019 | 200 | 2019 |
| Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems S Langner, F Häse, JD Perea, T Stubhan, J Hauch, LM Roch, ... Advanced Materials 32 (14), 1907801, 2020 | 182 | 2020 |
| Machine learning exciton dynamics F Häse, S Valleau, E Pyzer-Knapp, A Aspuru-Guzik Chemical Science 7 (8), 5139-5147, 2016 | 136 | 2016 |
| ChemOS: orchestrating autonomous experimentation LM Roch, F Häse, C Kreisbeck, T Tamayo-Mendoza, LPE Yunker, ... Science Robotics 3 (19), eaat5559, 2018 | 135 | 2018 |
| Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories F Häse, LM Roch, A Aspuru-Guzik Chemical science 9 (39), 7642-7655, 2018 | 127 | 2018 |
| Data-science driven autonomous process optimization M Christensen, LPE Yunker, F Adedeji, F Häse, LM Roch, T Gensch, ... Communications Chemistry 4 (1), 112, 2021 | 122 | 2021 |
| ChemOS: An orchestration software to democratize autonomous discovery LM Roch, F Häse, C Kreisbeck, T Tamayo-Mendoza, LPE Yunker, ... PLoS One 15 (4), e0229862, 2020 | 113 | 2020 |
| 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 | 101* | 2021 |
| How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry F Häse, IF Galván, A Aspuru-Guzik, R Lindh, M Vacher Chemical science 10 (8), 2298-2307, 2019 | 99 | 2019 |
| Machine learning directed drug formulation development P Bannigan, M Aldeghi, Z Bao, F Häse, A Aspuru-Guzik, C Allen Advanced Drug Delivery Reviews 175, 113806, 2021 | 93 | 2021 |
| On scientific understanding with artificial intelligence M Krenn, R Pollice, SY Guo, M Aldeghi, A Cervera-Lierta, P Friederich, ... Nature Reviews Physics 4 (12), 761-769, 2022 | 90 | 2022 |
| Machine learning for quantum dynamics: deep learning of excitation energy transfer properties F Häse, C Kreisbeck, A Aspuru-Guzik Chemical science 8 (12), 8419-8426, 2017 | 83 | 2017 |
| 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 | 69 | 2021 |
| Designing and understanding light-harvesting devices with machine learning F Häse, LM Roch, P Friederich, A Aspuru-Guzik Nature Communications 11 (1), 4587, 2020 | 64 | 2020 |
| Free energy analysis and mechanism of base pair stacking in nicked DNA F Häse, M Zacharias Nucleic acids research 44 (15), 7100-7108, 2016 | 58 | 2016 |
| Oscillatory active-site motions correlate with kinetic isotope effects in formate dehydrogenase P Pagano, Q Guo, C Ranasinghe, E Schroeder, K Robben, F Hase, H Ye, ... ACS catalysis 9 (12), 11199-11206, 2019 | 31 | 2019 |
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verified email at utoronto.ca
