Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning |
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| Authors: | Ruihao Yuan Zhen Liu Prasanna V. Balachandran Deqing Xue Yumei Zhou Xiangdong Ding Jun Sun Dezhen Xue Turab Lookman |
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| Affiliation: | 1. State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China;2. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA |
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| 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. |
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| Keywords: | active learning electrostrain machine learning optimal experimental design piezoelectric |
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