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Title: Accelerated Discovery of Large Electrostrains in BaTiO 3 ‐Based Piezoelectrics Using Active Learning

Journal Article · · Advanced Materials
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  1. State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University Xi'an 710049 China
  2. Theoretical Division Los Alamos National Laboratory Los Alamos NM 87545 USA

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 BaTiO 3 (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 (Ba 0.84 Ca 0.16 )(Ti 0.90 Zr 0.07 Sn 0.03 )O 3 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.

Sponsoring Organization:
USDOE
OSTI ID:
1420192
Journal Information:
Advanced Materials, Journal Name: Advanced Materials Vol. 30 Journal Issue: 7; ISSN 0935-9648
Publisher:
Wiley Blackwell (John Wiley & Sons)Copyright Statement
Country of Publication:
Germany
Language:
English
Citation Metrics:
Cited by: 204 works
Citation information provided by
Web of Science

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