  In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that
accommodates domain knowledge to tune exploration in the search space. Bayesian
optimization has recently emerged as a sample-efficient optimizer for many
intractable scientific problems. While various existing BO frameworks allow the
input of prior beliefs to accelerate the search by narrowing down the space,
incorporating such knowledge is not always straightforward and can often
introduce bias and lead to poor performance. Here we propose a simple approach
to incorporate structural knowledge in the acquisition function by utilizing an
additional deterministic surrogate model to enrich the approximation power of
the Gaussian process. This is suitably chosen according to structural
information of the problem at hand and acts a corrective term towards a
better-informed sampling. We empirically demonstrate the practical utility of
the proposed method by successfully injecting domain knowledge in a materials
design task. We further validate our method's performance on different
experimental settings and ablation analyses.
