Machine Learning‐Enhanced Prediction of Inorganic Semiconductor Bandgaps for Advancing Optoelectronic Technologies

Abstract

A pivotal challenge in advancing inorganics optoelectronic technologies, is the precise characterization of materials' electronic attributes, with the bandgap being a critical property. Conventional approaches, heavily reliant on time‐intensive and financially demanding experimental and computational methods, such as density functional theory (DFT) calculations, face limitations due to inherent estimation errors. Machine learning methodologies are developed for the prediction of bandgaps of inorganic semiconductors but most of them are employed for datasets created by DFT calculations, hence limiting their performance. Addressing this, the study leverages machine learning methodologies, harnessing both compositional and structural features, to predict the band gaps of inorganic semiconductors with enhanced accuracy. This advancement is reinforced by the employment of an experimental bandgap dataset, which, when integrated with structural descriptors obtained from the Materials Project, significantly improves prediction capabilities. This is evidenced by the model's exceptional performance across two distinct benchmark datasets. Furthermore, the model's adeptness in predicting formation energies underscores its versatility and applicability to a broad spectrum of electronic properties. These findings suggest that the predictive accuracy of this model can be further augmented through the inclusion of additional experimental bandgap measurements and the refinement of structural descriptors. This approach offers a promising and efficient alternative to traditional methodologies, potentially accelerating the development of optoelectronic technologies.