Artificial Intelligence-Driven Design and Sustainability of Selective Absorber Coatings for Solar Thermal Collectors: A Systematic Review

Artificial intelligence (AI) is increasingly applied to the design and optimization of solar thermal collectors, particularly in the development of selective absorber coatings. This systematic review analyzes recent advances (2020–2026) in AI-driven modeling, optimization, and sustainability strategies for solar thermal technologies following the PRISMA 2020 methodology. The results indicate that current research is largely dominated by Artificial Neural Networks and metaheuristic algorithms, mainly focused on short-term performance prediction and system-level optimization. However, durability, degradation mechanisms, and life-cycle sustainability metrics remain significantly underrepresented in AI-assisted design frameworks. From a materials perspective, recent studies highlight the emergence of multifunctional absorber surfaces, including thermochromic, self-cleaning, and multilayer coatings, often combined with AI-enabled monitoring and digital twin approaches. In addition, sustainable processing routes such as green sol–gel synthesis and low-temperature deposition show strong potential for reducing environmental impact when integrated with AI-based optimization. Nevertheless, the holistic integration of AI with sustainability metrics at the early design stage remains limited. Future research should therefore focus on hybrid and physics-informed AI frameworks capable of simultaneously addressing performance, durability, economic viability, and environmental impact in solar thermal collector design.