Desmond Klenam
Artificial Intelligence (AI) is reshaping the field of materials science and engineering by transforming how structural and functional materials are discovered, designed, and optimized. Traditional alloy development has long relied on trial-and-error methodologies, which are time-consuming, costly, and constrained by the complexity of multicomponent systems. This paper presents a comprehensive review of how AI, particularly machine learning (ML), is revolutionizing rational alloy design by enabling predictive modeling, inverse design, and multi-objective optimization across vast compositional and microstructural spaces. By integrating empirical, theoretical, computational, and data-driven paradigms, AI augments human intuition with computational precision, drastically reducing discovery timelines. Case studies illustrate how AI frameworks ranging from generative models to physics-informed ML and self-driving laboratories are accelerating breakthroughs in materials for aerospace, energy, and biomedical sectors. The paper also explores the synergistic integration of AI with high-throughput experimentation, domain knowledge, and open-access databases, enabling sustainable materials innovation. Although challenges persist regarding data quality, interpretability, and generalizability, the convergence of AI and materials science offers a transformative path forward in designing high-performance, environmentally resilient materials for the 21st century.