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Accelerated Discovery of Large Electrostrains in BaTiO 3 ‐Based Piezoelectrics Using Active Learning
Author(s) -
Yuan Ruihao,
Liu Zhen,
Balachandran Prasanna V.,
Xue Deqing,
Zhou Yumei,
Ding Xiangdong,
Sun Jun,
Xue Dezhen,
Lookman Turab
Publication year - 2018
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201702884
Subject(s) - piezoelectricity , materials science , key (lock) , tetragonal crystal system , density functional theory , field (mathematics) , space (punctuation) , electric field , nanotechnology , engineering physics , computer science , composite material , physics , crystal structure , crystallography , mathematics , quantum mechanics , computer security , pure mathematics , operating system , chemistry
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.