Development of a particle swarm optimization based support vector regression model for titanium dioxide band gap characterization

DOI: 10.1088/1674-4926/40/2/022803 Cite this Article

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Fig. 1. (Color online) Sensitivity of the developed PSO-SVR model on the number of generation keeping the number of population at 200.

Fig. 2. Correlation cross-plot between the estimated and experimental band gap.

Fig. 3. (Color online) Effect of cobalt-sulfur co-doping on the band gap of TiO₂ using the developed PSO-SVR model.

Fig. 4. (Color online) Effect of nickel-iodine co-doping on the band gap of TiO₂ using the developed PSO-SVR model.

Fig. 5. (Color online) Effect of tungsten doping on the band gap of TiO₂ using the developed PSO-SVR model.

Fig. 6. (Color online) Effect of indium doping on the band gap of TiO₂ using the developed PSO-SVR model.

Parameter	Lattice parameter a (Å)	Lattice parameter c (Å)	Band gap (eV)
Mean (eV)	3.783	9.498 928	2.943 27
Maximum (eV)	3.807	9.667	3.458
Minimum (eV)	3.763	9.363	1.75
Standard deviation	0.008	0.042 441	0.356 839
Correlation coefficient (%)	1.42	−42.80

Table 1. Statistical analysis of the dataset.

Table 2. Optimum model parameters.

Performance evaluation parameters	Training dataset	Testing dataset
RMSE (eV)	0.253	0.165
MAE (eV)	0.163	0.125
CC (%)	72.56	84.13

Table 3. Performance evaluation parameters and their corresponding values.

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