  Prediction of the electronic structure of functional materials is essential
for the engineering of new devices. Conventional electronic structure
prediction methods based on density functional theory (DFT) suffer from not
only high computational cost, but also limited accuracy arising from the
approximations of the exchange-correlation functional. Surrogate methods based
on machine learning have garnered much attention as a viable alternative to
bypass these limitations, especially in the prediction of solid-state band
gaps, which motivated this research study. Herein, we construct a random forest
regression model for band gaps of double perovskite materials, using a dataset
of 1306 band gaps computed with the GLLBSC (Gritsenko, van Leeuwen, van Lenthe,
and Baerends solid correlation) functional. Among the 20 physical features
employed, we find that the bulk modulus, superconductivity temperature, and
cation electronegativity exhibit the highest importance scores, consistent with
the physics of the underlying electronic structure. Using the top 10 features,
a model accuracy of 85.6% with a root mean square error of 0.64 eV is obtained,
comparable to previous studies. Our results are significant in the sense that
they attest to the potential of machine learning regressions for the rapid
screening of promising candidate functional materials.
