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Accelerating materials discovery using machine learning
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DOI：10.1016/j.jmst.2020.12.010.png)
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Abstract
En 中文
The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation, the traditional materials research mainly depended on the trial-and-error method, which is time-consuming and laborious. Recently, machine learning (ML) methods have made great progress in the researches of materials science with the arrival of the big-data era, which gives a deep revolution in human society and advance science greatly. However, there exist few systematic generalization and summaries about the applications of ML methods in materials science. In this review, we first provide a brief account of the progress of researches on materials science with ML employed, the main ideas and basic procedures of this method are emphatically introduced. Then the algorithms of ML which were frequently used in the researches of materials science are classified and compared. Finally, the recent meaningful applications of ML in metal materials, battery materials, photovoltaic materials and metallic glass are reviewed. (C) 2021 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Keywords:
Materials discovery
Materials design
Materials properties prediction
Machine learning
Data-driven
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