Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Significance Learning from data to accelerate the discovery of new materials is an outstanding challenge in materials science. However, in addition to data, a distinguishing aspect of materials science is that there exists a substantial body of knowledge in the form of phenomenological models and physical theories. We combine informatics and materials knowledge using results from Landau–Devonshire theory to guide experiments in the design of lead-free piezoelectrics with desired properties.