What is high-throughput virtual screening? A perspective from organic materials discovery EO Pyzer-Knapp, C Suh, R Gómez-Bombarelli, J Aguilera-Iparraguirre, ... Annual Review of Materials Research 45 (1), 195-216, 2015 | 324 | 2015 |
Learning from the harvard clean energy project: The use of neural networks to accelerate materials discovery EO Pyzer‐Knapp, K Li, A Aspuru‐Guzik Advanced Functional Materials 25 (41), 6495-6502, 2015 | 233 | 2015 |
Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space JM Hernández-Lobato, J Requeima, EO Pyzer-Knapp, A Aspuru-Guzik International conference on machine learning, 1470-1479, 2017 | 217 | 2017 |
Accelerating materials discovery using artificial intelligence, high performance computing and robotics EO Pyzer-Knapp, JW Pitera, PWJ Staar, S Takeda, T Laino, DP Sanders, ... npj Computational Materials 8 (1), 84, 2022 | 185 | 2022 |
Machine learning exciton dynamics F Häse, S Valleau, E Pyzer-Knapp, A Aspuru-Guzik Chemical Science 7 (8), 5139-5147, 2016 | 154 | 2016 |
Controlling the crystallization of porous organic cages: molecular analogs of isoreticular frameworks using shape-specific directing solvents T Hasell, JL Culshaw, SY Chong, M Schmidtmann, MA Little, KE Jelfs, ... Journal of the American Chemical Society 136 (4), 1438-1448, 2014 | 149 | 2014 |
The Harvard organic photovoltaic dataset SA Lopez, EO Pyzer-Knapp, GN Simm, T Lutzow, K Li, LR Seress, ... Scientific data 3 (1), 1-7, 2016 | 131 | 2016 |
Bayesian optimization for accelerated drug discovery EO Pyzer-Knapp IBM Journal of Research and Development 62 (6), 2: 1-2: 7, 2018 | 94 | 2018 |
A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials EO Pyzer-Knapp, GN Simm, AA Guzik Materials Horizons 3 (3), 226-233, 2016 | 93 | 2016 |
In silico Design of Supramolecules from Their Precursors: Odd–Even Effects in Cage-Forming Reactions KE Jelfs, EGB Eden, JL Culshaw, S Shakespeare, EO Pyzer-Knapp, ... Journal of the American Chemical Society 135 (25), 9307-9310, 2013 | 92 | 2013 |
Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences AP Carrieri, N Haiminen, S Maudsley-Barton, LJ Gardiner, B Murphy, ... Scientific reports 11 (1), 4565, 2021 | 89 | 2021 |
Predicted crystal energy landscapes of porous organic cages EO Pyzer-Knapp, HPG Thompson, F Schiffmann, KE Jelfs, SY Chong, ... Chemical Science 5 (6), 2235-2245, 2014 | 86 | 2014 |
Evolving the materials genome: How machine learning is fueling the next generation of materials discovery C Suh, C Fare, JA Warren, EO Pyzer-Knapp Annual Review of Materials Research 50 (1), 1-25, 2020 | 80 | 2020 |
Bayesian Self‐Optimization for Telescoped Continuous Flow Synthesis AD Clayton, EO Pyzer‐Knapp, M Purdie, MF Jones, A Barthelme, J Pavey, ... Angewandte Chemie 135 (3), e202214511, 2023 | 57 | 2023 |
Utilizing machine learning for efficient parameterization of coarse grained molecular force fields JL McDonagh, A Shkurti, DJ Bray, RL Anderson, EO Pyzer-Knapp Journal of chemical information and modeling 59 (10), 4278-4288, 2019 | 51 | 2019 |
Dynamic control of explore/exploit trade-off in Bayesian optimization D Jasrasaria, EO Pyzer-Knapp Intelligent Computing: Proceedings of the 2018 Computing Conference, Volume …, 2019 | 44 | 2019 |
An optimized intermolecular force field for hydrogen-bonded organic molecular crystals using atomic multipole electrostatics EO Pyzer-Knapp, HPG Thompson, GM Day Acta Crystallographica Section B: Structural Science, Crystal Engineering …, 2016 | 40 | 2016 |
Self-focusing virtual screening with active design space pruning DE Graff, M Aldeghi, JA Morrone, KE Jordan, EO Pyzer-Knapp, CW Coley Journal of Chemical Information and Modeling 62 (16), 3854-3862, 2022 | 35 | 2022 |
Roughness of molecular property landscapes and its impact on modellability M Aldeghi, DE Graff, N Frey, JA Morrone, EO Pyzer-Knapp, KE Jordan, ... Journal of Chemical Information and Modeling 62 (19), 4660-4671, 2022 | 32 | 2022 |
A multi-fidelity machine learning approach to high throughput materials screening C Fare, P Fenner, M Benatan, A Varsi, EO Pyzer-Knapp npj Computational Materials 8 (1), 257, 2022 | 28 | 2022 |