Assessment of TiO2 band gap from structural parameters using Artificial Neural Networks

Abstract Titanium dioxide is widely employed in photocatalysis reactions. Despite its variety of applications, it is still a challenge to modify it to work under visible light. Strategies have been investigated to increase the photocatalytic performance of TiO2. Usually, the aspects discussed are the ratio of crystalline phases, the crystal size, and the band gap value. However, these properties are generally empirically associated with each other. The present work was carried out to investigate the relation of those TiO2 properties on the band gap energy, also indexing its transition type. Due to the system complexity, all values were taken from the literature for Artificial Neural Network (ANN) topologies development. The dataset was carefully selected and inspected with different types of ANN, training algorithms, transfer functions, and numbers of hidden neurons. The network was evaluated by the Sum of Squared Error (SSE) and correlation coefficients (R2) for training and test data. The best model was 4-4-6-1 (corresponding to input, first hidden layer, second hidden layer, and output neurons, respectively). Although the network SSE (2.24) and R2 for training (84.3 %) were well fitted, the R2 test could only adjust 50.7 %. Therefore, questions about empirical reports of catalysis were raised and discussed, such as standards of characterization techniques of band gap measurement, and the influence of different crystalline phases in a single material.