AI-Driven Material Design and Discovery: Revolutionizing the Development of Novel Alloys and Composites

Artificial intelligence (AI) integration in material science led to fundamental transformations in the discovery process of new alloys and composites which simultaneously accelerated development while reducing expenses. The field of material development relies on excessive experimental practices and computer modeling according to traditional methods. Artificial intelligence methods using deep learning and machine learning (ML) enable accelerated large data set analysis which leads to material quality prediction and optimal material composition discovery and better performing material development. The artificial intelligence methods explore new material compositions through combination of high-throughput simulations and experimental data and generative algorithms to invent materials suitable for aerospace engineering and biomedical applications and renewable energy field. The combination of sustainable substitutes discovery alongside minimized resource consumption enables AI-driven discovery to enhance sustainability. Great as its transforming capabilities may be the method faces challenges such as sparse data and difficulty interpreting models along with lab validation needs. Resolving material discovery for high-performance alloys and composites in the following generation remains the central target of this research that examines AI-driven approaches along with their methods and potential future developments.