Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.
basic_science · Level V
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- Record sourced from PubMed, PMID 27821777.
- Also identified by PMC identifier 5127307.
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Abstract
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 BaTiO<sub>3</sub>-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba<sub>0.5</sub>Ca<sub>0.5</sub>)TiO<sub>3</sub>-Ba(Ti<sub>0.7</sub>Zr<sub>0.3</sub>)O<sub>3</sub>, with piezoelectric properties that show better temperature reliability than other BaTiO<sub>3</sub>-based piezoelectrics in our initial training data.