Abstract
Machine learning (ML) is an evolving scientific field of advanced statistical models and algorithms that are developed to imitate human intelligence by learning from data. In recent years, ML has successfully accelerated materials development and discovery in various engineering and technological applications. In this chapter, we provide a comprehensive overview of ML techniques that are used for materials modeling and design. We discuss different ML methods and algorithms along with their fundamental concepts, including feature selection methods and regression models, which are extensively implemented for materials discovery. We also review recent applications of ML techniques in performance prediction of materials, such as the glass transition temperature of conjugated polymers and protein structure. This chapter provides readers with an overview of ML methods and outlines possible practical usages of ML in materials research.