Unsupervised learning and pattern recognition in alloy design

Abstract

Machine learning has the potential to revolutionise alloy design by uncovering useful patterns in complex datasets and supplementing human expertise and experience. This review examines the role of unsupervised learning methods, including clustering, dimensionality reduction, and manifold learning, in the context of alloy design. While the use of unsupervised learning in alloy design is still in its early stages, these techniques offer new ways to analyse high-dimensional alloy data, uncovering structures and relationships that are difficult to detect with traditional methods. Using unsupervised learning, researchers can identify specific groups within alloy data sets that are not simple partitions based on metal compositions, and can help optimise and develop new alloys with customised properties. Incorporating these data-driven methods into alloy design speeds up the discovery process and reveals new connections that were not previously understood, significantly contributing to innovation in materials science. This review outlines the key scientific progress and future possibilities for using unsupervised machine learning in alloy design.