Applications of Artificial Intelligence in Materials Physics and Energy Systems

The rapid evolution of artificial intelligence (AI) has profoundly transformed materials physics and energy research. Conventional approaches to materials design, characterization, and process optimization are often hindered by high computational costs, extended experimental timelines, and limited predictive accuracy. AI-driven techniques, including machine learning, deep learning, and reinforcement learning, provide powerful data-driven tools for modeling complex systems, identifying patterns, and enabling accurate predictions. In materials physics, AI accelerates the discovery of novel compounds, predicts physical and chemical properties, and simulates atomic-scale interactions with unprecedented efficiency. In parallel, AI enhances energy systems through performance optimization, real-time process control, and fault detection across technologies such as batteries, fuel cells, and photovoltaic systems. This paper presents a comprehensive review of recent AI applications in materials science and energy, highlighting key algorithms, emerging trends, and future research directions toward intelligent, efficient, and sustainable energy solutions.