  Robotics and automation offer massive accelerations for solving intractable,
multivariate scientific problems such as materials discovery, but the available
search spaces can be dauntingly large. Bayesian optimization (BO) has emerged
as a popular sample-efficient optimization engine, thriving in tasks where no
analytic form of the target function/property is known. Here we exploit expert
human knowledge in the form of hypotheses to direct Bayesian searches more
quickly to promising regions of chemical space. Previous methods have used
underlying distributions derived from existing experimental measurements, which
is unfeasible for new, unexplored scientific tasks. Also, such distributions
cannot 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 used
to augment the surrogate model data, thus achieving better-informed sampling.
This process continues in a global versus local search fashion, organized in a
bilevel optimization framework. We validate the performance of our method on a
range of synthetic functions and demonstrate its practical utility on a real
chemical design task where the use of expert hypotheses accelerates the search
performance significantly.

Machine Learning;Human-Computer Interaction