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

Citations Over TimeTop 1% of 2018 papers

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 (Ba_0.84 Ca_0.16 )(Ti_0.90 Zr_0.07 Sn_0.03 )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.

Related Papers

• → A tetragonal high-pressure phase of PtAs2(2021)2 cited
• → Strong ellipticity for tetragonal system in linearly elastic solids(2008)8 cited
• → Quest for an optoelectronic material: tetragonal SixGe1−x films(2002)3 cited
• → SYNTHESES OF MONOCLINIC AND TETRAGONAL ZrO2 AT HIGH TEMPERATURES(1972)7 cited
• → The cubic limiting complexes of the tetragonal lattice complexes(2003)4 cited