Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors

Abstract

Quaternary III–V semiconductors are one of the most 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 of virtual screening of multinary III–V materials for optoelectronic applications.

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

“…Mondal et al [26] used ML to predict the properties of the bandgap for quaternary III-V semiconductors. The author emphasized that the most widely used ML model in semiconductor material bandgap prediction is SVM.…”

“…For example, support vector machine (SVM), random forest (RF), and other ensemble models have been important tools in electronic structure properties prediction. [26][27][28][29] Deep learning (DL) has been widely used in optoelectronic materials and often necessitates substantial volumes of data. Convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), and graph neural network (GNN) are mainly applied to properties prediction, [30][31][32] structure prediction, [33][34][35] image analysis, [36] and optimization.…”