Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.
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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, (Ba0.5Ca0.5)TiO3-Ba(Ti0.7Zr0.3)O3, with piezoelectric properties that show better temperature reliability than other BaTiO3-based piezoelectrics in our initial training data.
author list (cited authors)
Xue, D., Balachandran, P. V., Yuan, R., Hu, T., Qian, X., Dougherty, E. R., & Lookman, T.
complete list of authors
Xue, Dezhen||Balachandran, Prasanna V||Yuan, Ruihao||Hu, Tao||Qian, Xiaoning||Dougherty, Edward R||Lookman, Turab