  Designing functional molecules and advanced materials requires complex design
choices: tuning continuous process parameters such as temperatures or flow
rates, while simultaneously selecting 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. Here, we introduce Gryffin, a general purpose optimization framework
for the autonomous selection of categorical variables driven by expert
knowledge. Gryffin augments Bayesian optimization based on kernel density
estimation with smooth approximations to categorical distributions. Leveraging
domain knowledge in the form of physicochemical descriptors, Gryffin can
significantly accelerate the search for promising molecules and materials.
Gryffin can further highlight relevant correlations between the provided
descriptors to inspire physical insights and foster scientific intuition. In
addition to comprehensive benchmarks, we demonstrate the capabilities and
performance of Gryffin on three examples in materials science and chemistry:
(i) the discovery of non-fullerene acceptors for organic solar cells, (ii) the
design of hybrid organic-inorganic perovskites for light harvesting, and (iii)
the identification of ligands and process parameters for Suzuki-Miyaura
reactions. Our results suggest that Gryffin, in its simplest form, is
competitive with state-of-the-art categorical optimization algorithms. However,
when leveraging domain knowledge provided via descriptors, Gryffin outperforms
other approaches while simultaneously refining this domain knowledge to promote
scientific understanding.
