Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

Abstract Experimental search for high-temperature ferroelectric perovskites is a challenging task due to the vast chemical space and lack of predictive guidelines. Here, we demonstrate a two-step machine learning approach to guide experiments in search of x Bi $$[ {{\mathrm{Me}}_y' {\mathrm{Me}}_{(1 - y)}'' } ]$$ [Mey′Me(1-y)″] O 3 –(1 − x )PbTiO 3 -based perovskites with high ferroelectric Curie temperature. These involve classification learning to screen for compositions in the perovskite structures, and regression coupled to active learning to identify promising perovskites for synthesis and feedback. The problem is challenging because the search space is vast, spanning ~61,500 compositions and only 167 are experimentally studied. Furthermore, not every composition can be synthesized in the perovskite phase. In this work, we predict x , y , Me′, and Me″ such that the resulting compositions have both high Curie temperature and form in the perovskite structure. Outcomes from both successful and failed experiments then iteratively refine the machine learning models via an active learning loop. Our approach finds six perovskites out of ten compositions synthesized, including three previously unexplored {Me′Me″} pairs, with 0.2Bi(Fe 0.12 Co 0.88 )O 3 –0.8PbTiO 3 showing the highest measured Curie temperature of 898 K among them.