Artificial Intelligence and Its Immense Relevance to Composite Materials: A Snapshot

Composite materials have brought highly welcomed changes to a variety of industries, including aerospace, automotive engineering, and renewable energy, by offering an unmatched mechanical strength-to-weight ratio, multi functionality capabilities, and other excellent performance roles. Composite structures constitute deliberate mixtures of unique reinforcements, such as carbon or glass fibers, with unique and engineered matrices, which create composite performance metrics that are not achievable by monolithic metals or ceramics. Nevertheless, the design, optimization, and lifetime management of these materials are still constrained by multiscale complexity, computational complexity, and experimental resource intensity. In this article, we assess how artificial intelligence (AI) - including machine learning (ML), physics-informed neural networks (PINNs), graphs, and quantum-based approaches is changing the composites material lifecycle. We systematically examine the way AI is changing the game for accelerating property prediction, enabling inverse design, optimizing manufacturing conditions, and informing intelligent structural health monitoring (SHM). For example, with sufficient training, graph neural networks (GNNs) are now modelling interfacial adhesion with comparable accuracy to density functional theory (DFT) but with time savings of three orders of magnitude. Generative diffusion models trained on synthetic microstructures are being used to prototype wind turbine blades and aerospace panels in less than one month, instead of 12-18 months, which was typical of traditional iterative means. Further, transformer-based multimodal inspection systems have achieved >95% classification accuracy on 17 defect types while exceeding the spatial resolution of NDT/type without exceeding 15× resolution limits of pre-NDT and situational standards specifications. We present GNoME (Graph Neural composite Optimization and Modeling Environment), which is a seamless interface for combining multiscale simulation, generative architectures, and experimental feedback. GNoME represents an important step from purely data-driven learning to hybrid approaches by including physics, uncertainty quantification, and domain-specific priors. We also highlight some persistent challenges, such as sparse data environments, black-box model interpretability, and regulatory hurdles. The review ends with a technology roadmap outlining quantum-enhanced AI, ethical governance in industrial applications, and sustainable composite design paradigms. This extensive review synthesized over 300 published studies published between 2018 and 2022 with an intention that it will help researchers and practitioners utilize practical frameworks for applying AI in intelligent, adaptive, and sustainable composites for the next generation.