Machine Learning for Structural Materials

Abstract

The development of structural materials with outstanding mechanical response has long been sought for innumerable industrial, technological, and even biomedical applications. However, these compounds tend to derive their fascinating properties from a myriad of interactions spanning multiple scales, from localized chemical bonding to macroscopic interactions between grains. This diversity has limited the ability of researchers to develop new materials on a reasonable timeline. Fortunately, the advent of machine learning in materials science has provided a new approach to analyze high-dimensional space and identify correlations among the structure-composition-property-processing relationships that may have been previously missed. In this review, we examine some successful examples of using data science to improve known structural materials by analyzing fatigue and failure, and we discuss approaches to develop entirely new classes of structural materials in complex composition spaces including high-entropy alloys and bulk metallic glasses. Highlighting the recent advancement in this field demonstrates the power of data-driven methodologies that will hopefully lead to the production of market-ready structural materials.