Band gap predictions of double perovskite oxides using machine learning

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 stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented. Tuning the band gap of perovskite oxides is key for achieving tailored electronic properties in transistors, LEDs, photovoltaics, and scintillators. Here, by exploring all chemical combinations of 68 elements, machine learning is used to identify and predict stable synthesizable cubic perovskites with desired band gap values.