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Clyde Fare

Self-Organizing and Stochastic Behaviors During the Regeneration of Hair Stem Cells
2011 · Maksim V. Plikus, Ruth E. Baker, et al. · Science · 216
Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
2024 · Yuri Alexeev, Maximilian Amsler, et al. · Future Generation Computer Systems · 140
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Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery
2020 · Changwon Suh, Clyde Fare, et al. · Annual Review of Materials Research · 103
A multi-fidelity machine learning approach to high throughput materials screening
2022 · Clyde Fare, Peter Fenner, et al. · npj Computational Materials · 93
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PyLDM - An open source package for lifetime density analysis of time-resolved spectroscopic data
2017 · Gabriel F. Dorlhiac, Clyde Fare, et al. · PLoS Computational Biology · 27
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Physics-constrained machine learning for thermal turbulence modelling at low Prandtl numbers
2022 · Matilde Fiore, Lilla Koloszar, et al. · International Journal of Heat and Mass Transfer · 25
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X-ray Free Electron Laser Determination of Crystal Structures of Dark and Light States of a Reversibly Photoswitching Fluorescent Protein at Room Temperature
2017 · Christopher Hutchison, Violeta Cordon-Preciado, et al. · International Journal of Molecular Sciences · 20
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Coincidence timing of femtosecond optical pulses in an X-ray free electron laser
2017 · Alvaro Sanchez-Gonzalez, Allan S. Johnson, et al. · Journal of Applied Physics · 17
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
2020 · Clyde Fare, Lukas Turcani, et al. · Physical Chemistry Chemical Physics · 16
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Radical-Triggered Reaction Mechanism of the Green-to-Red Photoconversion of EosFP
2020 · Clyde Fare, Letong Yuan, et al. · The Journal of Physical Chemistry B · 10
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A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets
2022 · Fare, Clyde, Fenner, Peter, et al. · arXiv (Cornell University) · 4
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History Matching Reservoir Models With Many Objective Bayesian Optimization
2024 · Steven Samoil, Clyde Fare, et al. · Applied AI Letters · 3
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An Adaptive Approach to Bayesian Optimization with Setup Switching Costs
2024 · Stefan Pricopie, Richard Allmendinger, et al. · Lecture notes in computer science · 3
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Expensive optimization with production-graph resource constraints
2022 · Stefan Pricopie, Richard Allmendinger, et al. · Genetic and Evolutionary Computation Conference (GECCO) · 3
Modelling Mobile Signal Strength by Combining Geospatial Big Data and Artificial Intelligence
2020 · Paolo Fraccaro, Matt Benatan, et al. · International Conference on Vision, Image and Signal Processing (ICVISP) · 1
Efficient simulation of complex fluid phase diagrams with Bayesian optimization
2025 · Steven G. Arturo, Clyde Fare, et al. · Digital Discovery
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Providing Machine Learning Potentials with High Quality Uncertainty Estimates
2025 · Sumer, Zeynep, McDonagh, James L., et al. · arXiv (Cornell University)
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Bayesian Optimization with Setup Switching Cost
2024 · Stefan Pricopie, Richard Allmendinger, et al. · Genetic and Evolutionary Computation Conference Companion (GECCO Companion)
An adaptive approach to Bayesian Optimization with switching costs
2024 · Pricopie, Stefan, Allmendinger, Richard, et al. · arXiv (Cornell University)
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MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery
2024 · Clyde Fare, George K. Holt, et al. · Proceedings of the AAAI Conference on Artificial Intelligence
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Hybrid methods for molecular spectroscopy and reactivity
2017 · Fare, Clyde · Spiral (Imperial College London)
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Works count: 22
Citation count: 681
H-index: 10
I10-index: 10
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