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 xBi[Formula: see text]O₃-(1 - x)PbTiO₃-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 pred

SUBMITTER: Balachandran PV 

PROVIDER: S-EPMC5920103 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature