Machine Learning Approches for Evaluating the Properties of Materials

Abstract

Machine Learning for Materials Science is a primer on the subject that also delves into the specifics of where ML might be applied to materials science research. With a focus on where to collect data and some of the issues when choosing a strategy, this article includes example approaches for ML applied to experiments and modeling, such as the first steps in the procedure for constructing an ML solution for a materials science problem. The lengthy cycles of development, inefficiencies, and higher costs of conventional techniques of material discovery, such as the density functional theory- based and empirical trials and errors approach, make it impossible for materials research to keep up with modern advances. Hence, machine learning is extensively employed in material detection, material design, and material analysis because of its cheap computing cost and fast development cycle, paired with strong data processing and good prediction performance. This article summarizes recent applications of ML algorithms within different material science fields, discussing the advancements that are needed for widespread application, and details the critical operational procedures involved in evaluating the features of materials using ML.