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Robotics and automation offer massive accelerations for solving intractable,multivariate scientific problems such as materials discovery, but the availablesearch spaces can be dauntingly large. Bayesian optimization (BO) has emergedas a popular sample-efficient optimization engine, thriving in tasks where noanalytic form of the target function/property is known. Here we exploit experthuman knowledge in the form of hypotheses to direct Bayesian searches morequickly to promising regions of chemical space. Previous methods have usedunderlying distributions derived from existing experimental measurements, whichis unfeasible for new, unexplored scientific tasks. Also, such distributionscannot capture intricate hypotheses. Our proposed method, which we call HypBO,uses expert human hypotheses to generate an improved seed of samples.Unpromising seeds are automatically discounted, while promising seeds are usedto augment the surrogate model data, thus achieving better-informed sampling.This process continues in a global versus local search fashion, organized in abilevel optimization framework. We validate the performance of our method on arange of synthetic functions and demonstrate its practical utility on a realchemical design task where the use of expert hypotheses accelerates the searchperformance significantly.

HypBO: Expert-Guided Chemist-in-the-Loop Bayesian Search for New Materials