Accelerated Discovery of Large Electrostrains in BaTiO3-Based Piezoelectrics Using Active Learning

Ruihao Yuan, Zhen Liu, Prasanna V. Balachandran, Deqing Xue, Yumei Zhou, Xiangdong Ding, Jun Sun, Dezhen Xue, Turab Lookman

Research output: Contribution to journalArticlepeer-review

312 Scopus citations

Abstract

A key challenge in guiding experiments toward materials with desired properties is to effectively navigate the vast search space comprising the chemistry and structure of allowed compounds. Here, it is shown how the use of machine learning coupled to optimization methods can accelerate the discovery of new Pb-free BaTiO3 (BTO-) based piezoelectrics with large electrostrains. By experimentally comparing several design strategies, it is shown that the approach balancing the trade-off between exploration (using uncertainties) and exploitation (using only model predictions) gives the optimal criterion leading to the synthesis of the piezoelectric (Ba0.84Ca0.16)(Ti0.90Zr0.07Sn0.03)O3 with the largest electrostrain of 0.23% in the BTO family. Using Landau theory and insights from density functional theory, it is uncovered that the observed large electrostrain is due to the presence of Sn, which allows for the ease of switching of tetragonal domains under an electric field.

Original languageEnglish
Article number1702884
JournalAdvanced Materials
Volume30
Issue number7
DOIs
StatePublished - 15 Feb 2018
Externally publishedYes

Keywords

  • active learning
  • electrostrain
  • machine learning
  • optimal experimental design
  • piezoelectric

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