Researchers in the chemistry and materials science sectors have begun to take aim at BO problems that feature unknown constraints. Wakabayashi et al. investigate molecular beam epitaxy of SrRuO 3 , a widely used metallic electrode in oxide electronics. Experimental failures are defined as experimental parameter settings for which the SrRuO 3 phase did not form, preventing measurement of the resistivity objective. The authors consider two approaches to deal with the experimental failures: the so called "floor-padding trick", in which infeasible measurement's objectives are replaced by the worst objective value observed so far (naïve-replace approach described in Sec. III.B), as well as an approach in which the feasibility-aware acquisition function is a product of the expected improvement criterion and the probability that a parameter space point is feasible as learned by a binary classifier (FWA approach described in Sec. III.B). In concurrent work with ours, Khatamsaz and coworkers presented an approach for multi-objective design of multi-principal element alloys for potential use in next-generation gas turbine blades. The approach is based on the author's previous work, in which a novel Bayesian classification method embedded in a multifidelity learning framework was introduced. This allowed the authors to effectively fuse together multiple information sources, including the constraint boundaries, into a single experiment planning algorithm which was employed to efficiently optimize DFT-derived ductility indicators across a Refractory Multi-Principal-Element Alloy (MPEA) space. the constraint function, c(x). In this section, we briefly describe our binary classification model.