Electronic properties prediction enhancement of 36 ternary III-IB-VI alloys using a deep feed-forward neural network

Abstract

The electronic properties of 36 ternary III-IB-VI monolayers have been investigated using a correlation study, in which a comprehensive study of the band structure of these alloys is provided. To this end, a combination of density functional theory calculations and a deep feed-forward neural network is conducted. Principal component analysis and data augmentation are also implemented as pre-processing. The former reduces the size of features to mitigate redundancy, and the latter can prevent overfitting. The deep feed-forward neural network is then used for the final prediction. During the study, bandgaps of the alloys are predicted indirectly and compared with directly obtained bandgaps. The results of the predictions show a maximum precision of 0.00089 for nMSE using the deep feed-forward neural network, which confirms strong dependencies between the electronic properties of the alloys.