METALS AND METAL MATRIX COMPOSITES |
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A Review of the Application of Machine Learning in Material Structure and Performance Prediction |
HOU Tengyue1, SUN Yanhui1, SUN Shupeng1, XIAO Ying1, ZHENG Yangong2, WANG Jing3, DU Haiying4, WU Juanxin4
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1 College of Information & Communication Engineering, Dalian Minzu University, Dalian 116600,Liaoning, China 2 Faculty of Electrical Engineer and Computer Science, Ningbo University, Ningbo 315020, Zhejiang,China 3 School of Electronic Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China 4 College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600,Liaoning, China |
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Abstract Materials are considered as an important indicator of contemporary progress. Materials with different structures have different physical and chemical properties, which influence their functions. Several experiments have been carried out to achieve materials with optimal designs; cross-over trial is such a method that it is usually adopted. However, it is quite complicated, because it is not suitable for highly non-linear or large-scale combination processes, and it also makes it challenging to reveal rare attributes of materials. Recent advancements in computer science and machine learning have enabled the development of methods to coordinate the efficiency and cost of development in fields such as materials discovery, structural analysis, property prediction, and reverse design, demonstrating remarkable untapped potential in materials science. Machine learning has its limitations, such as the need to ensure efficient collection of data sets, information processing of heterogeneous data sets, establishment of prediction models based on lightweight data sets, and reliability forecasting of properties of materials. Not only are these problems the key issues in the field that urgently need to be resolved, but they also form the crux of research studies on machine learning for the prediction of structures and properties of materials. Thus, solving these problems can boost the progress of materials science. In recent years, the number of papers on the application of machine learning in material science has increased exponentially. Moreover, several studies have employed machine learning to guide the synthesis of novel materials with superior properties. Vector machines, neural networks, and other machine learning algorithms can be used to form data sets and build models for predicting material properties, such as absorption, electrical properties, catalytic performance, mechanical properties, and thermal performance. These developments have considerably contributed toward the development of materials science and have enabled achievement of major breakthroughs. This review summarizes findings of machine learning studies for prediction of structures and properties of materials science, discussing data sources, prediction models, and conclusions, and forecasting the development of machine learning in the materials domain in the future.
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Published: 25 March 2022
Online: 2022-03-21
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Fund:Natural Science Foundation of Liaoning Province,China(20180550634). |
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