no code implementations • 14 May 2024 • Stefan Pricopie, Richard Allmendinger, Manuel Lopez-Ibanez, Clyde Fare, Matt Benatan, Joshua Knowles
We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost.
no code implementations • 11 Aug 2022 • Clyde Fare, Peter Fenner, Edward O. Pyzer-Knapp
Multifidelity and multioutput optimisation algorithms are of active interest in many areas of computational design as they allow cheaper computational proxies to be used intelligently to aid experimental searches for high-performing species.
no code implementations • 17 Sep 2018 • Clyde Fare, Lukas Turcani, Edward O. Pyzer-Knapp
Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation.