H. Saha, A.K. Saha
Haldia Institute of Technology,
India
Keywords: AI, machine learning, materials science, sustainable energy, renewable energy, solar cells, batteries, fuel cells, energy storage, computational materials science
Summary:
The transition to sustainable energy systems is critically dependent on the development of advanced materials that can efficiently harness, store, and convert energy. Artificial Intelligence (AI) has emerged as a transformative tool in the design and optimization of materials for energy applications, offering unprecedented capabilities for discovering new compounds and improving existing technologies. This paper explores the role of AI-enhanced materials in sustainable energy systems, focusing on their application in energy generation, storage, and conversion. We discuss how machine learning, data-driven modeling, and high-throughput simulations enable the identification of novel materials with tailored properties for photovoltaic cells, batteries, fuel cells, and thermoelectric devices. Furthermore, we examine the integration of AI in materials discovery pipelines, accelerated prototyping, and real-time performance monitoring, highlighting the potential for reducing development timelines and increasing material efficiency. Challenges, such as data scarcity and the need for domain-specific models, are also addressed. The paper concludes by outlining future directions for AI in materials science, emphasizing the need for interdisciplinary collaboration to meet global energy demands and mitigate environmental impact.