查看文章

The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E_g, a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E_g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E_g < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically …