  We present new multi-test Bayesian optimization models and algorithms for use
in large scale material screening applications. Our screening problems are
designed around two tests, one expensive and one cheap. This paper differs from
other recent work on multi-test Bayesian optimization through use of a flexible
model that allows for complex, non-linear relationships between the cheap and
expensive test scores. This additional modeling flexibility is essential in the
material screening applications which we describe. We demonstrate the power of
our new algorithms on a family of synthetic toy problems as well as on real
data from two large scale screening studies.
