Guided materials discovery examples have been increasingly prevalent in the literature. Some of these are experimental and computational adaptive design schemes using high-throughput experimental or computational (e.g. density functional theory (DFT) and finite element modeling ) methods. Extraordinary predictions, or predictions which perform close to or better than top performers in the training data are rarer . Kauwe et al. describes how it is even rarer to discover materials that are fundamentally (as opposed to incrementally) different from existing materials, i.e. discover new chemistries. A suite of regression models are available for use as the backbone for a mate-fying "discovery-rich" and "discovery-poor" design spaces in the context of high-performance rather than chemical distinctiveness.