Machine learning optimized efficient graphene-based ultra-broadband solar absorber for solar thermal applications.

We designed an ultra-broadband graphene absorber structure with the applied resonator design based on the Al-AlSb-Cr structure, and a thin effective layer of graphene is inserted. To develop the role of the graphene in solar absorbers, the current structure investigates above 98% for 1500 nm bandwidth and 2800 nm (overall bandwidth) for 93.68%. In this study, the procedure of the investigated design in flow chat configuration, the multi-step presentation of the developed layers, and the analysis of the used parameters will be involved. The design is optimized using machine learning algorithm. The optimized design shows good performance compared to the other system. The newly investigated graphene design can be absorbed not only in visible places but also in near-infrared energy and ultraviolet zones. The other applications of the light trapping process, photovoltaic devices, and energy harvesting can also be used.