Machine Learning-Assisted Estimation of the Photoantioxidant Activities of Bare, Mg, Cu, and Mg/Cu Dual-Doped ZnO

Abstract

Green-synthesized pure zinc oxide (ZnO), Mg-doped ZnO, Cu-doped ZnO, and Mg/Cu dual-doped ZnO using aqueous leaf extract of Ziziphus mauritiana were analyzed for their antioxidant activities. In this study, the data-driven approach has been used to estimate the photoantioxidant activities of ZnO, Mg-doped ZnO, Cu-doped ZnO, and Mg/Cu dual-doped ZnO based on the experimental data and synthetic data generated through simulations. Three different machine learning models, including artificial neural network, extreme gradient boosting, and automated machine learning, were explored and compared for both data sets. These models were validated by using external validation and applicability domain methods based on the values of coefficient of determination, root mean square, and mean absolute errors. The performance of the machine learning techniques showed that photoantioxidant activities could be predicted accurately from the input variables such as types of dopants, percentage of dopants, average crystallite size, lighting condition, and concentration of antioxidants (photocatalyst). Doping and the lighting condition were found to have a more significant impact on the values of photoantioxidant activities of the ZnO, Mg-doped ZnO, Cu-doped ZnO, and Mg/Cu dual-doped ZnO in comparison to other variables. Based on three artificial neural network models, the variables for Mg doping and the lighting condition had weights with values ranging between 1.1 and 2.9.