Cost-effective materials discovery: Bayesian optimization across multiple information sources

Applications of Bayesian optimization to problems in the materials sciences have primarily focused on consideration of a single source of data, such as DFT, MD, or experiments. This work shows how it is possible to incorporate cost-effective sources of information with more accurate, but expensive, sources as a means to significantly accelerate materials discovery in the computational sciences. Specifically, we compare the performance of three surrogate models for multi-information source optimization (MISO) in combination with a cost-sensitive knowledge gradient approach for the acquisition function: a multivariate Gaussian process regression, a cokriging method exemplified by the intrinsic coregionalization model, and a new surrogate model we created, the Pearson-rcoregionalization model. To demonstrate the effectiveness of this MISO approach to the study of commonly encountered materials science problems, we show MISO results for three test cases that outperform a standard efficient global optimization (EGO) algorithm: a challenging benchmark function (Rosenbrock), a molecular geometry optimization, and a binding energy maximization. We outline factors that affect the performance of combining different information sources, including one in which a standard EGO approach is preferable to MISO.