Machine Learning-Driven Design of Smart Materials with Adaptive Properties for Advanced Applications

Machine Learning (ML) is revolutionizing the design of smart materials by allowing fast discovery and optimization of adaptive materials, supplanting slow, conventional trial-and-error processes. ML methods such as deep learning and reinforcement learning make data-driven predictions of material properties, speeding innovation in shape memory alloys, self-healing polymers, piezoelectric materials, and metamaterials. Case studies highlight how machine learning (ML) is being used to build innovations in the fields of energy harvesting, medicinal implants, aerospace, and AR/VR. There are still challenges with experimental verification, interpretability of models, and the lack of data. These have to be addressed through interdisciplinary collaboration and merging of AI-based methods with physics-based models. Potential to revolutionize fields such as electronics, healthcare, and aerospace into next-generation materials with improved performance and efficiency, the future of machine learning (ML)-based smart materials is highly promising