Accelerated search for BaTiO₃-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau-Devonshire theory, we demonstrate our approach for BaTiO₃-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba_0.5Ca_0.5)TiO₃-Ba(Ti_0.7Zr_0.3)O₃, with piezoelectric properties that show better temperature reliability than other BaTiO₃-based piezoelectrics in our initial training data.