Abstract

Generative artificial intelligence (generative AI) has emerged as a transformative force in materials science, revolutionizing the way materials are discovered, designed, and optimized. This abstract explores the current state of generative AI applications in materials science, highlighting its profound impact on materials discovery, property prediction, and manufacturing processes. Key advancements include the use of AI models to accelerate the exploration of vast chemical spaces, predict material properties with high accuracy, and optimize manufacturing techniques such as additive manufacturing. Despite these advancements, challenges such as data quality, interpretability of AI models, and ethical considerations remain. Looking forward, future directions in generative AI and materials science include multi-objective optimization, integration with quantum computing, and innovations in materials recycling. The ongoing evolution of generative AI promises to unlock new opportunities for innovation and sustainable development in materials science, shaping the future of technology and industry.