Authors: Assistant professor Benasir Begam.F, Agalya.A
Abstract: 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.
