Modeling the Specific Surface Area of Doped Spinel Ferrite Nanomaterials Using Hybrid Intelligent Computational Method

Spinel ferrites nanomaterials are magnetic semiconductors with excellent chemical, magnetic, electrical, and optical properties which have rendered the materials useful in many technological driven applications such as solar hydrogen production, data storage, sensing, converters, inductors, spintronics, catalysts. The surface area of these contributes significantly to their targeted well observed physical chemical features. Experimental doping has shown a great potential enhancing tuning specific spinel ferrite while attributed experimental challenges call for viable theoretical model that can estimate doped high degree precision. This work develops stepwise regression (STWR) hybrid genetic algorithm-based support vector (GBSVR) intelligent estimating using lattice parameter size nanoparticle descriptors models. developed GBSVR performs better than STWR performance improvement 7.51% 22.68%, respectively, correlation coefficient root mean square error metrics when validated experimentally measured nanomaterials. investigates influence nickel, yttrium, lanthanum nanoparticles on different classes nanomaterials, obtained results agree excellently values. accuracy precision characterizing would be immense importance nanomaterial prediction circumvention stress coupled reduced cost.