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

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

Research output: Contribution to journal › Article › peer-review

135 Scopus citations

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.

Original language	English
Pages (from-to)	13301-13306
Number of pages	6
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	113
Issue number	47
DOIs	https://doi.org/10.1073/pnas.1607412113
State	Published - 22 Nov 2016
Externally published	Yes

Keywords

Bayesian learning
Materials informatics
Morphotropic phase boundary
Pb-free materials
Piezoelectric materials

Access to Document

10.1073/pnas.1607412113

Cite this

@article{ad0a7792dcda4f09a69bc44b0cf9e58e,

title = "Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning",

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, (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.",

keywords = "Bayesian learning, Materials informatics, Morphotropic phase boundary, Pb-free materials, Piezoelectric materials",

author = "Dezhen Xue and Balachandran, \{Prasanna V.\} and Ruihao Yuan and Tao Hu and Xiaoning Qian and Dougherty, \{Edward R.\} and Turab Lookman",

year = "2016",

month = nov,

day = "22",

doi = "10.1073/pnas.1607412113",

language = "英语",

volume = "113",

pages = "13301--13306",

journal = "Proceedings of the National Academy of Sciences of the United States of America",

issn = "0027-8424",

publisher = "National Academy of Sciences",

number = "47",

}

Accelerated search for BaTiO₃-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning. / Xue, Dezhen; Balachandran, Prasanna V.; Yuan, Ruihao et al.
In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 113, No. 47, 22.11.2016, p. 13301-13306.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

AU - Xue, Dezhen

AU - Balachandran, Prasanna V.

AU - Yuan, Ruihao

AU - Hu, Tao

AU - Qian, Xiaoning

AU - Dougherty, Edward R.

AU - Lookman, Turab

PY - 2016/11/22

Y1 - 2016/11/22

N2 - 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.

AB - 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.

KW - Bayesian learning

KW - Materials informatics

KW - Morphotropic phase boundary

KW - Pb-free materials

KW - Piezoelectric materials

UR - https://www.scopus.com/pages/publications/84996483972

U2 - 10.1073/pnas.1607412113

DO - 10.1073/pnas.1607412113

M3 - 文章

AN - SCOPUS:84996483972

SN - 0027-8424

VL - 113

SP - 13301

EP - 13306

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

IS - 47

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this