Journal
ADVANCED MATERIALS
Volume 30, Issue 7, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.201702884
Keywords
active learning; electrostrain; machine learning; optimal experimental design; piezoelectric
Categories
Funding
- National Natural Science Foundation of China [51571156, 51671157, 51302209, 51621063, 51431007, 51320105014]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT13034]
- National Key Research and Development Program of China [2017YFB0702401]
- LDRD program at Los Alamos National Laboratory
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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)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.
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