Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm-Based Support Vector Regression

Tin (II) sulfide (SnS) is a metal chalcogenide semiconducting material with fascinating and admirable physical features for practical applications in solid-state batteries, photodetectors, gas sensors, optoelectronic devices, emission transistors, photocatalysis among others. The energy gap of SnS semiconductor nanomaterial that facilitates its usefulness many can be adjusted through dopant incorporation which results crystal lattice distortion at various crystallite sizes the semiconductor. This work employs parameter descriptors to develop hybrid genetic algorithm (GA) support vector regression (SVR) intelligent model determining doped semiconductors. predictive strength developed GA-SVR compared stepwise algorithm- (STRA-) based using different performance evaluation parameters. performs better than STRA on root mean square error, absolute correlation coefficient improvement 70.68%, 67.63%, 20.98%, respectively, testing set data. Influence dopants experimental conditions were investigated model, while obtained values gaps agree measured values. models demonstrate high degree potentials terms accuracy, precision, ease implementation fosters their real-life applicability estimating stress circumvention.