AI Driven Discovery and Design of Smart Materials

Artificial Intelligence (AI) is revolutionising the discovery, characterisation, and application of smart materials. This review comprehensively explores AI’s role in smart materials research, focusing on Machine Learning (ML) for property prediction, generative models for inverse design, and autonomous research systems. AI advancements have accelerated the optimisation of materials like shape memory alloys, piezoelectric materials, and self-healing polymers, enhancing their functionality. However, challenges such as data quality, model interpretability, and scalability persist. This paper addresses these innovations, their limitations, and future directions for AI-driven materials science. Major Findings: AI-driven methods, including ML and generative models, significantly enhance smart material discovery by improving prediction accuracy and enabling inverse design. Autonomous systems streamline experimental processes, but data quality and model interpretability remain critical challenges. Emerging trends like foundation models and active learning promise further advancements.