Data-driven photocatalytic degradation activity prediction with Gaussian process

Abstract

Photocatalysis has emerged as a powerful technology with beneficial impacts on the fields of science and engineering. To date, most photocatalysis research are experimentally-based that strongly rely on various experimental conditions. As the coronavirus pandemic hit the world in 2020, research and experiments were disrupted in various scientific disciplines. During these unprecedented times, machine learning plays a vital role in the continuity of photocatalysis research, notably for researchers under physical access restrictions. More specifically, machine learning is capable of predicting the photocatalytic efficiency and analysing the photocatalytic activity. In recent work, it was demonstrated that a Support Vector Regression (SVR) model succeeded in predicting the efficiency of methyl tert-butyl ether (MTBE) photodegradation using titanium dioxide (TiO2) as a photocatalyst, achieving a Root Mean Square Error (RMSE) of 5%. In this work, we investigate the applicability of the Gaussian Process (GP) technique to predict the photodegradation efficiency of contaminants catalyzed by pure and doped-titanium dioxide (TiO2); and we compare their performance with the current state-of-the-art SVR. Within this context, we discuss the foundations of both the machine learning models, as well as demonstrate how photocatalysis researchers can apply them to solving relevant problems in the field of photocatalysis.