机器学习在材料信息学中的应用综述

doi:10.11896/cldb.19110175

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Abstract In the face of huge material design space, the traditional methods based on theoretical research, experimental analysis and computational simulation cannot keep up with the development of new materials with high performance. In recent years, the combination of material database and machine learning has led to the progress of material informatics and the development of material science. At present, the use of data-driven machine learning algorithms to establish material performance prediction models, and then apply them to materials screening and new material deve-lopment research has attracted widespread attention from scholars. Using the machine learning framework to build the material research and design platform to analyze and predict the material big data resources has become an important means to develop new materials.
Machine learning is applied to a series of difficulties faced by materials science, including the calculation or automatic extraction of material characteristics according to the predicted objects, the acquisition and preprocessing of experimental and computational data with different precision, the selection or development of appropriate machine learning prediction models and training algorithms, estimating the reliability of prediction effect and predictive performance, and deal with material machine learning problems with the unique characteristics of small data, heterogeneous data and unbalanced data. At present, the focus of research is to collect relevant data sets, construct feature representations based on physical principles to train machine learning models and apply the latest techniques of machine learning to material informatics for different material properties.
Machine learning has been used in photovoltaic, thermoelectric, semiconductor, organic materials and other materials design fields. The process of new material discovery is greatly accelerated by using machine learning algorithm to train the prediction model of material performance and to screen existing material database or search new material. At present, scientists at home and abroad have carried out a series of research with the help of statistical reasoning and machine learning algorithms, developed a variety of material characterization methods suitable for predicting the properties of different materials, and applied the latest machine learning and artificial intelligence methods, including deep learning, Bayesian network, etc. Breakthrough achievements have been made in the field of multi-functional material design.
This paper mainly introduces the related research and application of machine learning methods in material performance prediction, including the most commonly used material database resources, various applicable machine learning algorithms and application examples, and the common problems encountered by machine learning in material performance prediction. Finally, the development status of material informatics at home and abroad is summarized and the future development is prospected.

Key words： materials informatics material science material properties machine learning big data

Published: 24 December 2020

CLC number:	TP181
	TB302

Fund:This work was supported by the National Natural Science Foundation of China (51741101).

About author:: Chengcheng Niu received the B.S. degree in mechanical engineering from Shandong Jiaotong University in 2017. She is currently pursuing M.S. degree in mecha-nical engineering at the Key Laboratory of Advanced Manufacturing Technology (Ministry of Education), Guizhou University under the supervision of Prof. Jianjun Hu. Her research has focused on materials informa-tics.
Jianjun Hu received the B.S. and M.S. degrees of mechanical engineering in 1995 and 1998 respectively from Wuhan University of Technology, China. He received the Ph.D. of computer science in 2004 from Michigan State University in the area of machine learning and evolutionary computation. He worked as postdoctoral fellow at Purdue University and University of Southern California from 2004 to 2007. He is currently associate professor at the Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States and also visiting professor at School of Mechanical Engineering, Guizhou University. His research interests include machine learning, deep learning, data mining, evolutionary computation, bioinformatics, and material informatics. His research has been supported by NSF Career Award and NSF China. Dr. Hu is also the associate editors of Nature Scientific Report, PLOS ONE, and BMC Bioinformatics.

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Cite this article:

NIU Chengcheng,LI Shaobo,HU Jianjun, et al. Application of Machine Learning in Material Informatics: a Survey[J]. Materials Reports, 2020, 34(23): 23100-23108.

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http://www.mater-rep.com/EN/10.11896/cldb.19110175 OR http://www.mater-rep.com/EN/Y2020/V34/I23/23100

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