Application of Machine Learning in Material Synthesis and Property Prediction

Author:

Huang Guannan1,Guo Yani1,Chen Ye1,Nie Zhengwei1ORCID

Affiliation:

1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China

Abstract

Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.

Funder

Natural Science Foundation of Jiangsu Province, China

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Materials Science

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