Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 113, Issue 47, Pages 13301-13306Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1607412113
Keywords
piezoelectric materials; materials informatics; Bayesian learning; morphotropic phase boundary; Pb-free materials
Categories
Funding
- Los Alamos National Laboratory
- Laboratory Directed Research and Development program [20140013DR]
- Center for Nonlinear Studies
- 973 Program [2012CB619401]
- National Natural Science Foundation [51302209, 51431007, 51320105014, 51321003]
- National Science Foundation [1553281]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1553281] Funding Source: National Science Foundation
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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)O-3, with piezoelectric properties that show better temperature reliability than other BaTiO3-based piezoelectrics in our initial training data.
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