Machine Learning-Enabled Superior Energy Storage in Ferroelectric Films with a Slush-Like Polar State
Ruihao Yuan, Abinash Kumar, Shihao Zhuang, Nicholas Cucciniello, Teng Lü, Deqing Xue, Deqing Xue, Aubrey Penn, Alessandro R. Mazza, Q. X. Jia, Yun Liu, Dezhen Xue, Dezhen Xue, Jinshan Li, Jia‐Mian Hu, James M. LeBeau, Aiping Chen
Abstract
Heterogeneities in structure and polarization have been employed to enhance the energy storage properties of ferroelectric films. The presence of nonpolar phases, however, weakens the net polarization. Here, we achieve a slush-like polar state with fine domains of different ferroelectric polar phases by narrowing the large combinatorial space of likely candidates using machine learning methods. The formation of the slush-like polar state at the nanoscale in cation-doped BaTiO 3 films is simulated by phase field simulation and confirmed by aberration-corrected scanning transmission electron microscopy. The large polarization and the delayed polarization saturation lead to greatly enhanced energy density of 80 J/cm 3 and transfer efficiency of 85% over a wide temperature range. Such a data-driven design recipe for a slush-like polar state is generally applicable to quickly optimize functionalities of ferroelectric materials.