A hybrid intelligent scheme for estimating band gap of doped titanium dioxide semiconductor using crystal lattice distortion

Wide band gap of titanium dioxide (TiO₂) semiconductor remains a challenge in photo-catalysis application where light absorption ability of the semiconductor is desired to go beyond ultra-violent region without enhancement of charge recombination rate. In order to address this challenge, two-layer feedforward neural network is proposed for doped titanium dioxide band gap estimation using sensitivity based linear learning method (SBLLM) hybridized with gravitational search algorithm (GSA) for efficient optimization of the number of epoch and hidden neurons. The performance sensitivity of the proposed hybrid SBLLM-GSA model on the gravitational constant and the number of agents are simulated and discussed. The hybrid SBLLM-GSA model outperforms ordinary SBLLM with performance improvement of 13.13% on the basis of root mean square error. The ability of the proposed SBLLM-GSA model to generalize to unseen data was assessed by feeding the model with the crystal lattice parameters of indium doped TiO₂, copper-indium co-doped TiO₂ as well as sulfur doped TiO₂ semiconductor and the obtained band gaps agree well with the measured values. The outcomes of this work indicate the effectiveness of the proposed hybrid model in estimating the band gap of TiO₂ semiconductor for efficiency and performance improvement in photo-catalysis application as well as other applications where band gap adjustment is essential for performance enhancement of the semiconductor.