Machine learning-based prediction of optical band gap in WO3 and its derivatives for semiconducting applications

Abstract

Band gap engineering plays a vital role in designing a functional semiconductor for specific applications in the arena of material science. Recently, machine learning algorithms are turning out very useful tools for the prediction of the optical band gaps of transition metal oxides (TMO). TMO are the most commonly used semiconductors for a wide range of applications, and Tungsten Oxide (WO3) is one of the potential candidates of the TMO family. Herein, we have reported an alternative way to determine the bandgap of WO3 and its derivatives for the fabrication of novel semiconducting devices. Conventional methods; mainly based on density functional theory (DFT) and UV-Vis Spectroscopy sometimes suffers from higher computational and experimentation costs respectively. To avoid such many other complications, we have tested ten machine learning algorithms and further verified that gradient boost regression (GBR) turned out to be the most suitable algorithm which exhibited root-mean-square error of around 7.99E-05 eV and an R2 score near 1 (0.9999). The exceptional precision of the regression model is achieved by relying solely on structural descriptors and training the model with a dataset that exclusively comprises experimentally measured band gaps. Estimation of the bandgaps with the help of such machine learning algorithms is practicable for large set virtual screening of WO3 and WO3-based derivatives for the range of applications in the field of energy storage, optoelectronics, sensing, and many other semiconducting applications.