  Quaternary III-V semiconductors are one of the major promising material
classes in optoelectronics. The bandgap and its character, direct or indirect,
are the most important fundamental properties determining the performance and
characteristics of optoelectronic devices. Experimental approaches screening a
large range of possible combinations of III- and V-elements with variations in
composition and strain are impractical for every target application. We present
a combination of accurate first-principles calculations and machine learning
based approaches to predict the properties of the bandgap for quaternary III-V
semiconductors. By learning bandgap magnitudes and their nature at density
functional theory accuracy based solely on the composition and strain features
of the materials as an input, we develop a computationally efficient yet highly
accurate machine learning approach that can be applied to a large number of
compositions and strain values. This allows for a computationally efficient
prediction of a vast range of materials under different strains, offering the
possibility for virtual screening of multinary III-V materials for
optoelectronic applications.
