Bayesian Screening: Multi-test Bayesian Optimization Applied to in silico Material Screening
This work addresses material screening challenges for researchers, but it is incremental as it builds on existing multi-test Bayesian optimization methods with added flexibility.
The paper tackles the problem of large-scale material screening by developing multi-test Bayesian optimization models that handle complex, non-linear relationships between cheap and expensive tests, demonstrating their effectiveness on synthetic and real-world data.
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.