Accelerated discovery of supertetragonal perovskites with giant polarization via machine learning

Abstract Ferroelectric perovskites with giant spontaneous polarization have extensive applications in electronic devices, energy conversion, sensor and so on. However, the rapid discovery of new perovskites with giant polarization remains an open challenge especially when thousands of candidates are treated. Here, combining machine learning (ML) and first-principles calculations, we successfully predict 8 perovskites with giant polarization from 2021 different possible compounds, among which seven candidates have never been reported before. These perovskites have large c/a ratio and giant polarization compared to the reported ferroelectric perovskites, and room temperature stability. Among them, the polarization of SnFeO 3 with G-AFM magnetic ordering is as high as 138.63 µC/cm 2 . The non-magnetic SrPbO 3 and magnetic EuSnO 3 not only exhibit giant polarization, but also possess band gaps close to the ideal value for photovoltaic applications, showing great potential in the field of ferroelectric photovoltaics. Besides, polarity and metallicity coexist in SnFeO 3 and CaTaO 3 , which are suggested to have potential applications in fields such as spintronics and superconductivity. This work thus provides an effective strategy for discovering new functional materials.