Materials informatics tasks are characterized by small, sparse, noisy, multi-scale, heterogeneous, and high-dimensional datasets . The search spaces associated with these tasks are often nonlinearly correlated, discrete, and/or non-linearly constrained. Some representative examples are dopant concentration interactions, experimental instrument limitations, and adherence to chemical parsimony (i.e., the unlikelihood of finding materials with more than 5 or 6 elements present), respec-tively. Due to small/expensive-to-sample datasets, Bayesian optimization (BO) is often chosen for materials discovery and process optimization problems for its excellent search efficiency. BO is an adaptive design technique that involves leveraging prior belief about the solution to a problem and updating the belief in the context of new information. One of the greatest strengths of Bayesian models via e.g., Gaussian processes is the elegant trade-off between exploiting high-performance regions and exploring high-uncertainty regions through acquisition functions 1 .