Abstract

The advancement of solar technologies hinges on the development of smart materials and coatings that optimize solar absorption and thermal conductivity, thereby enhancing overall system efficiency. The integration of artificial intelligence (AI) with material science presents a transformative framework for accelerating innovation through data-driven design, highthroughput screening, and predictive modeling. This chapter elucidates how AI-driven personalization enables the precise tailoring of material properties to varying environmental and operational conditions, fostering adaptive and high-performance solar energy solutions. Key challenges, including algorithmic bias, data privacy, and regulatory compliance, are critically examined to underscore the importance of ethical governance and social inclusivity in deploying AI-enhanced solar materials. Emphasizing multidisciplinary collaboration, the chapter highlights emerging computational tools and cloud-based platforms that facilitate seamless material discovery and real-world application. By bridging technical advancements with ethical and societal considerations, this comprehensive approach aims to drive equitable and sustainable energy transitions, positioning AI-enabled smart materials as pivotal components in the future of solar energy technologies.

Introduction

The global pursuit of sustainable energy solutions has placed solar technologies at the forefront of renewable energy research and deployment [1]. Central to enhancing the efficiency of solar devices is the advancement of smart materials and coatings capable of maximizing solar absorption and optimizing thermal conductivity [2]. These materials are designed to interact dynamically with their environment, improving the conversion of solar radiation into usable energy while minimizing thermal losses. Recent innovations have shown that conventional approaches relying solely on experimental methods are limited by slow trial-and-error cycles and high costs [3]. This challenge has prompted a shift toward integrating computational tools and data-driven methods to accelerate material discovery and optimize performance [4]. Among these, artificial intelligence (AI) has emerged as a powerful enabler, providing sophisticated algorithms capable of analyzing vast datasets, predicting material behaviors, and guiding the design of tailored coatings and composites that meet specific operational demands [5].

Artificial intelligence technologies facilitate the rapid screening of potential materials by leveraging big data analytics, machine learning, and high-throughput computational techniques [6]. These tools can efficiently navigate complex material spaces, identifying optimal compositions and structures that exhibit superior solar absorption and thermal conductivity properties [7]. In addition to enhancing speed, AI algorithms improve accuracy by learning from experimental and simulation data, enabling predictive models that reduce uncertainty in material performance. Cloud-based collaborative platforms further support this innovation by allowing multidisciplinary teams across geographies to share data, tools, and insights in real time [8]. The fusion of AI with advanced simulation methods fosters an environment where iterative improvement is continuous, propelling solar material research beyond traditional boundaries [9]. Such approaches not only expedite development but also open possibilities for personalized solar solutions adapted to local climates and installation conditions, thereby improving energy capture and system longevity [10].