Artículos con órdenes de acceso público - Edward O. Pyzer-KnappMás información
No disponibles en ningún lugar: 5
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
Órdenes: US Department of Energy
Bayesian optimization for accelerated drug discovery
EO Pyzer-Knapp
IBM Journal of Research and Development 62 (6), 2: 1-2: 7, 2018
Órdenes: UK Science and Technology Facilities Council
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), 2018
Órdenes: UK Science and Technology Facilities Council
Modelling Mobile Signal Strength by Combining Geospatial Big Data and Artificial Intelligence
P Fraccaro, M Benatan, K Reusch, C Fare, B Edwards, E Pyzer-Knapp
Proceedings of the 2020 4th International Conference on Vision, Image and …, 2020
Órdenes: UK Science and Technology Facilities Council
Smc samplers for bayesian optimisation and discovery of additive kernel structure
A Chatzopoulou, ÁF García-Fernández, E Pyzer-Knapp, S Maskell
2021 IEEE 24th International Conference on Information Fusion (FUSION), 1-8, 2021
Órdenes: UK Engineering and Physical Sciences Research Council
Disponibles en algún lugar: 32
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
Órdenes: US Department of Energy
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
Órdenes: US Department of Energy
Machine learning exciton dynamics
F Häse, S Valleau, E Pyzer-Knapp, A Aspuru-Guzik
Chemical Science 7 (8), 5139-5147, 2016
Órdenes: US Department of Energy
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
Órdenes: UK Engineering and Physical Sciences Research Council
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
Órdenes: US Department of Energy
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
Órdenes: US Department of Energy
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
Órdenes: UK Engineering and Physical Sciences Research Council
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
Órdenes: UK Science and Technology Facilities Council
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
Órdenes: UK Engineering and Physical Sciences Research Council, European Commission
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
Órdenes: UK Science and Technology Facilities Council
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
Órdenes: UK Engineering and Physical Sciences Research Council
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
Órdenes: UK Science and Technology Facilities Council
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
Órdenes: UK Engineering and Physical Sciences Research Council, European Commission
A fast parallel particle filter for shared memory systems
A Varsi, J Taylor, L Kekempanos, EP Knapp, S Maskell
IEEE Signal Processing Letters 27, 1570-1574, 2020
Órdenes: UK Engineering and Physical Sciences Research Council, UK Science and …
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
Órdenes: UK Science and Technology Facilities Council
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