  Bayesian optimization is used in many areas of AI for the optimization of
black-box processes and has achieved impressive improvements of the state of
the art for a lot of applications. It intelligently explores large and complex
design spaces while minimizing the number of evaluations of the expensive
underlying process to be optimized. Materials science considers the problem of
optimizing materials' properties given a large design space that defines how to
synthesize or process them, with evaluations requiring expensive experiments or
simulations -- a very similar setting. While Bayesian optimization is also a
popular approach to tackle such problems, there is almost no overlap between
the two communities that are investigating the same concepts. We present a
survey of Bayesian optimization approaches in materials science to increase
cross-fertilization and avoid duplication of work. We highlight common
challenges and opportunities for joint research efforts.
