Machine learning application in the life time of materials

TL;DR

This paper reviews how machine learning accelerates materials discovery, design, and failure analysis by leveraging experimental and computational data, highlighting current methods, applications, and challenges in the field.

Contribution

It provides a comprehensive overview of machine learning applications in materials science, including historical context, current techniques, and future challenges.

Findings

Machine learning enhances materials discovery and property prediction.

ML methods are used in synthesis, failure detection, and analysis.

Limitations and challenges of ML in materials science are discussed.

Abstract

Materials design and development typically takes several decades from the initial discovery to commercialization with the traditional trial and error development approach. With the accumulation of data from both experimental and computational results, data based machine learning becomes an emerging field in materials discovery, design and property prediction. This manuscript reviews the history of materials science as a disciplinary the most common machine learning method used in materials science, and specifically how they are used in materials discovery, design, synthesis and even failure detection and analysis after materials are deployed in real application. Finally, the limitations of machine learning for application in materials science and challenges in this emerging field is discussed.