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Accelerated search for BaTiO₃-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning
Ist Teil von
Proceedings of the National Academy of Sciences - PNAS, 2016-11, Vol.113 (47), p.13301-13306
Ort / Verlag
United States: National Academy of Sciences
Erscheinungsjahr
2016
Link zum Volltext
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
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.