  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.
