Abstract
Cuprous oxide (Cu
2
O) is a p-type metal oxide semiconducting material with potential in photovoltaic and photocatalysis applications due to its excellent absorption capacity in visible region and tunable energy gap. Experimental synthesis and energy gap characterization of thin film cuprous oxide semiconductor with desired dopants and varying experimental conditions for enhanced photocatalytic as well as photovoltaic activities are laborious and consume appreciable precious resources. This work hybridizes particle swarm optimization method with support vector regression algorithm for computing energy gap of thin film cuprous oxide semiconductor using the thickness of thin film and distorted lattice parameter as descriptors. The predictions of swarm-based support vector regression (S-SVR) model are compared with estimates of stepwise regression (SR) model while S-SVR shows superior performance of 39.47 %, 36.20 % and 114.41 % on testing data samples over SR model using root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC), respectively. The developed S-SVR model is characterized with 0.9559 CC, 0.0586 MAE and 0.028 RMSE on the basis of training samples. The developed S-SVR and SR models were further validated using external data samples while the developed S-SVR demonstrates excellent agreement with the measured values. The convincing precision demonstrated by S-SVR model would be of indispensable significance in determining energy gap of cuprous oxide semiconductor (for photocatalytic applications in pollutant removal, solar cell, gas sensors and thin film transistors) with appreciable quickness and reduced cost coupled with experimental difficulty circumvention.