Edward O. Pyzer-Knapp
Edward O. Pyzer-Knapp
IBM Research Global Lead AI Enriched Modelling and Simulation| Editor in Chief, Applied AI Letters
Verified email at uk.ibm.com
Cited by
Cited by
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, 195-216, 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
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
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
Machine learning exciton dynamics
F Häse, S Valleau, E Pyzer-Knapp, A Aspuru-Guzik
Chemical science 7 (8), 5139-5147, 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
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
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
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
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
Streaming histogram sketching for rapid microbiome analytics
WPM Rowe, AP Carrieri, C Alcon-Giner, S Caim, A Shaw, K Sim, JS Kroll, ...
Microbiome 7 (1), 1-13, 2019
Bayesian optimization for accelerated drug discovery
EO Pyzer-Knapp
IBM Journal of Research and Development 62 (6), 2: 1-2: 7, 2018
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
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, 1-25, 2020
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
C Fare, L Turcani, EO Pyzer-Knapp
Physical Chemistry Chemical Physics 22 (23), 13041-13048, 2020
Dynamic Control of Explore/Exploit Trade-Off in Bayesian Optimization
D Jasrasaria, EO Pyzer-Knapp
Science and Information Conference, 1-15, 2018
Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions
F Cipcigan, AP Carrieri, EO Pyzer-Knapp, R Krishna, YW Hsiao, M Winn, ...
The Journal of chemical physics 148 (24), 241744, 2018
Efficient and scalable batch Bayesian optimization using K-means
M Groves, EO Pyzer-Knapp
arXiv preprint arXiv:1806.01159, 2018
Space-filling curves as a novel crystal structure representation for machine learning models
D Jasrasaria, EO Pyzer-Knapp, D Rappoport, A Aspuru-Guzik
arXiv preprint arXiv:1608.05747, 2016
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), 1-18, 2021
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