  In this big data era, the use of large dataset in conjunction with machine
learning (ML) has been increasingly popular in both industry and academia. In
recent times, the field of materials science is also undergoing a big data
revolution, with large database and repositories appearing everywhere.
Traditionally, materials science is a trial-and-error field, in both the
computational and experimental departments. With the advent of machine
learning-based techniques, there has been a paradigm shift: materials can now
be screened quickly using ML models and even generated based on materials with
similar properties; ML has also quietly infiltrated many sub-disciplinary under
materials science. However, ML remains relatively new to the field and is
expanding its wing quickly. There are a plethora of readily-available big data
architectures and abundance of ML models and software; The call to integrate
all these elements in a comprehensive research procedure is becoming an
important direction of material science research. In this review, we attempt to
provide an introduction and reference of ML to materials scientists, covering
as much as possible the commonly used methods and applications, and discussing
the future possibilities.
