Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Dezhen Xue; Prasanna V Balachandran; Ruihao Yuan; Tao Hu; Xiaoning Qian; Edward R Dougherty; Turab Lookman

doi:10.1073/pnas.1607412113

Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Proc Natl Acad Sci U S A. 2016 Nov 22;113(47):13301-13306. doi: 10.1073/pnas.1607412113. Epub 2016 Nov 7.

Authors

Dezhen Xue^{1

2}, Prasanna V Balachandran¹, Ruihao Yuan², Tao Hu³, Xiaoning Qian³, Edward R Dougherty³, Turab Lookman⁴

Affiliations

¹ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545.
² State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China.
³ Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843.
⁴ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545; txl@lanl.gov.

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 BaTiO₃-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba_0.5Ca_0.5)TiO₃-Ba(Ti_0.7Zr_0.3)O₃, with piezoelectric properties that show better temperature reliability than other BaTiO₃-based piezoelectrics in our initial training data.

Keywords: Bayesian learning; Pb-free materials; materials informatics; morphotropic phase boundary; piezoelectric materials.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.