Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO-Based Ferroelectrics.

Descriptors play a central role in constructing composition-structure-property relationships to guide materials design. We propose a material descriptor, δτ, for the composition dependence of the Curie temperature () on single doping elements in BaTiO ferroelectrics, which is then generalized to a linear combination of multiple dopants in the solid solutions. The descriptor δτ depends linearly on the Curie temperature and also serves to separate the ferroelectric phase from the relaxor phase. We compare δτ to other commonly used descriptors such as the tolerance factor, electronegativity, and ionic displacement. By using regression analysis on our assembled experimental data, we show how it outperforms other descriptors. We use the trained machine-learned models to predict compositions in our search space with the largest ferroelectric, dielectric, and piezoelectric properties, namely, , electrostrain, and recoverable energy storage density. We experimentally verify our predictions for and classification into ferroelectrics and relaxors by synthesizing and characterizing six solid solutions in BaTiO ferroelectrics. Our definition of δτ can shed light on the design of knowledge-based descriptors in other systems such as Pb-based and Bi-based solid solutions.