Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.
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.
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545.|State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China.
Dezhen
D
Xue
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545.
Prasanna V
PV
Balachandran
State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China.
Ruihao
R
Yuan
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843.
Tao
T
Hu
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843.
Xiaoning
X
Qian
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843.
Edward R
ER
Dougherty
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545; txl@lanl.gov.
Turab
T
Lookman
Bayesian learning; Pb-free materials; materials informatics; morphotropic phase boundary; piezoelectric materials