A short review of machine learning in materials engineering: state of art, recent trends

Abstract

This review provides the current state and new developments in the use of machine learning in materials engineering. It starts with basic machine learning methods and looks at how these methods are used in important areas of materials research, such as material discovery, property prediction, and performance optimization. Emphasizing recent trends, it discusses the role of big data, the emergence of explainable artificial intelligence, and advances in computational methodologies that are shaping future directions. The views shared aim to improve the understanding of how machine learning speeds up materials innovation, boosts predictive abilities, and allows data-driven decision-making in materials engineering.