| Self-organizing and stochastic behaviors during the regeneration of hair stem cells MV Plikus, RE Baker, CC Chen, C Fare, D De La Cruz, T Andl, PK Maini, ... Science 332 (6029), 586-589, 2011 | 208 | 2011 |
| 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 | 66 | 2020 |
| PyLDM-An open source package for lifetime density analysis of time-resolved spectroscopic data GF Dorlhiac, C Fare, JJ van Thor PLoS computational biology 13 (5), e1005528, 2017 | 29 | 2017 |
| X-ray free electron laser determination of crystal structures of dark and light states of a reversibly photoswitching fluorescent protein at room temperature CDM Hutchison, V Cordon-Preciado, RML Morgan, T Nakane, J Ferreira, ... International Journal of Molecular Sciences 18 (9), 1918, 2017 | 19 | 2017 |
| 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 | 18 | 2020 |
| Physics-constrained machine learning for thermal turbulence modelling at low Prandtl numbers M Fiore, L Koloszar, C Fare, MA Mendez, M Duponcheel, Y Bartosiewicz International Journal of Heat and Mass Transfer 194, 122998, 2022 | 15 | 2022 |
| Coincidence timing of femtosecond optical pulses in an X-ray free electron laser A Sanchez-Gonzalez, AS Johnson, A Fitzpatrick, CDM Hutchison, C Fare, ... Journal of Applied Physics 122 (20), 2017 | 15 | 2017 |
| 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 | 13 | 2022 |
| Radical-triggered reaction mechanism of the green-to-red photoconversion of EosFP C Fare, L Yuan, V Cordon-Preciado, JJ Michels, MJ Bearpark, P Rich, ... The Journal of Physical Chemistry B 124 (36), 7765-7778, 2020 | 7 | 2020 |
| A principled method for the creation of synthetic multi-fidelity data sets C Fare, P Fenner, EO Pyzer-Knapp arXiv preprint arXiv:2208.05667, 2022 | 4 | 2022 |
| Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions Y Alexeev, M Amsler, P Baity, MA Barroca, S Bassini, T Battelle, D Camps, ... arXiv preprint arXiv:2312.09733, 2023 | 3 | 2023 |
| 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 | 2 | 2020 |
| Early experiment stopping for batch Bayesian optimization in industrial processes EO Pyzer-Knapp, C Fare US Patent 11,644,816, 2023 | 1 | 2023 |
| MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery C Fare, GK Holt, L Chiazor, M Smyrnakis, R Tracey, L Hoang Proceedings of the AAAI Conference on Artificial Intelligence 38 (21), 23787 …, 2024 | | 2024 |
| arXiv: Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions Y Alexeev, B Gropp, I Sitdikov, MA Barroca, K Moon, S Economou, ... | | 2023 |
| Driving Data Generation in Molecular Discovery Through Development of Benchmark Reinforcement Learning Environments C Fare, L Chiazor, NL Hoang INFORMS Annual Meeting, 2023 | | 2023 |
| Expensive optimization with production-graph resource constraints: a first look at a new problem class S Pricopie, R Allmendinger, M López-Ibáñez, C Fare, M Benatan, ... Proceedings of the Genetic and Evolutionary Computation Conference, 840-848, 2022 | | 2022 |
| Haystack–a computational molecular data notebook C Fare, M Bearpark | | |
Edward O. Pyzer-KnappIBM Research Global Lead AI Enriched Modelling and Simulation| Editor in Chief, Applied AI LettersVerified email at uk.ibm.com
