Machine Learning In Material Science for Microstructural Analysis, Property Prediction, and Alloy Design

Machine learning (ML) is transforming material science by shifting the traditional empirical and simulation-driven
approaches to a data-centric paradigm. This review presents an integrated overview of how ML methods are applied in
microstructure recognition, material property prediction, and alloy design. We discuss key learning paradigms such as
supervised, unsupervised, and deep learning, with emphasis on convolutional neural networks (CNNs), autoencoders, and
generative models. Representative studies are cited to illustrate applications in predictive modeling and image-based analysis. We highlight challenges related to data scarcity, model interpretability, and integration of physical principles. The review concludes with future directions, including autonomous materials discovery platforms and hybrid physics-informed ML models.