Gryffin: An algorithm for Bayesian optimization for categorical variables informed by physical intuition with applications to chemistry

26 Mar 2020Florian HäseLoïc M. RochAlán Aspuru-Guzik

Designing functional molecules and advanced materials requires complex interdependent design choices: tuning continuous process parameters such as temperatures or flow rates, while simultaneously selecting categorical variables like catalysts or solvents. To date, the development of data-driven experiment planning strategies for autonomous experimentation has largely focused on continuous process parameters despite the urge to devise efficient strategies for the selection of categorical variables to substantially accelerate scientific discovery... (read more)

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