Machine Learning Band Gaps of Doped-TiO₂ Photocatalysts from Structural and Morphological Parameters

Titanium dioxide (TiO₂) photocatalysts in the form of thin films are of great interest due to their tunable optical band gaps, E _g's, which are promising candidates for applications of visible-light photocatalytic activities. Previous studies have shown that processing conditions, dopant types and concentrations, and different combinations of the two have great impacts on structural, microscopic, and optical properties of TiO₂ thin films. The lattice parameters and surface area are strongly correlated with E _g values, which are conventionally simulated and studied through first-principle models, but these models require significant computational resources, particularly in complex situations involving codoping and various surface areas. In this study, we develop the Gaussian process regression model for predictions of anatase TiO₂ photocatalysts' energy band gaps based on the lattice parameters and surface area. We explore 60 doped-TiO₂ anatase photocatalysts with E _g's between 2.280 and 3.250 eV. Our model demonstrates a high correlation coefficient of 99.99% between predicted E _g's and their experimental values and high prediction accuracy as reflected through the prediction root-mean-square error and mean absolute error being 0.0012 and 0.0010% of the average experimental E _g, respectively. This modeling method is simple and straightforward and does not require a lot of parameters, which are advantages for applications and computations.