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

被引：111

作者：

Xue, Dezhen ^{[1
,2
]}

Balachandran, Prasanna V. ^{[1
]}

Yuan, Ruihao ^{[2
]}

Hu, Tao ^{[3
]}

Qian, Xiaoning ^{[3
]}

Dougherty, Edward R. ^{[3
]}

Lookman, Turab ^{[1
]}

机构：

[1] Los Alamos Natl Lab, Div Theoret, Los Alamos, NM 87545 USA

[2] Xi An Jiao Tong Univ, State Key Lab Mech Behav Mat, Xian 710049, Peoples R China

[3] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA

来源：

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 2016年 / 113卷 / 47期

基金：

美国国家科学基金会;

关键词：

piezoelectric materials; materials informatics; Bayesian learning; morphotropic phase boundary; Pb-free materials; SPONTANEOUS POLARIZATION; TEMPERATURE; DESIGN; CLASSIFICATION; GENE;

D O I：

10.1073/pnas.1607412113

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：13301 / 13306

页数：6

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