Review: machine learning approaches for diverse alloy systems

Abstract The integration of machine learning (ML) into alloy design has revolutionized the discovery and optimization of advanced materials by enabling high-throughput, data-driven methodologies. This review systematically examines recent advancements in ML applications across diverse alloy systems, including steels, aluminum alloys, magnesium alloys, nickel-based superalloys, high-entropy alloys (HEAs), shape memory alloys, and metallic glasses. We categorize ML approaches into supervised, unsupervised, and reinforcement learning paradigms, detailing their specific implementations for property prediction, phase stability analysis, and composition optimization. Advanced techniques, such as inverse design frameworks and physics-informed ML models, have demonstrated substantial improvements in predictive accuracy and interpretability by integrating domain knowledge with data-driven approaches. The review further explores the synergy between ML and traditional computational methods, including CALPHAD-based thermodynamic modeling and density functional theory (DFT), enhancing the reliability of property predictions. We highlight case studies where ML-driven strategies have successfully accelerated alloy discovery, optimized mechanical properties, and identified novel compositions with tailored performance metrics. Additionally, we address key challenges in ML-driven alloy design, including data scarcity, feature selection, model interpretability, and the necessity for standardized benchmarking datasets. By providing a comprehensive evaluation of current methodologies and emerging trends, this review underscores the transformative role of ML in advancing next-generation alloy design and manufacturing, ultimately enabling the rapid development of high-performance materials for aerospace, energy, biomedical, and structural applications.