Machine learning in materials science
Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs....
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-09-01
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| Series: | InfoMat |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/inf2.12028 |
| _version_ | 1811208828698492928 |
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| author | Jing Wei Xuan Chu Xiang‐Yu Sun Kun Xu Hui‐Xiong Deng Jigen Chen Zhongming Wei Ming Lei |
| author_facet | Jing Wei Xuan Chu Xiang‐Yu Sun Kun Xu Hui‐Xiong Deng Jigen Chen Zhongming Wei Ming Lei |
| author_sort | Jing Wei |
| collection | DOAJ |
| description | Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application. |
| first_indexed | 2024-04-12T04:28:18Z |
| format | Article |
| id | doaj.art-cbb0452f04fb498e992305fffa7ed895 |
| institution | Directory Open Access Journal |
| issn | 2567-3165 |
| language | English |
| last_indexed | 2024-04-12T04:28:18Z |
| publishDate | 2019-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | InfoMat |
| spelling | doaj.art-cbb0452f04fb498e992305fffa7ed8952022-12-22T03:48:00ZengWileyInfoMat2567-31652019-09-011333835810.1002/inf2.12028Machine learning in materials scienceJing Wei0Xuan Chu1Xiang‐Yu Sun2Kun Xu3Hui‐Xiong Deng4Jigen Chen5Zhongming Wei6Ming Lei7State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing ChinaState Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing ChinaState Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing ChinaState Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing ChinaState Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing ChinaZhejiang Provincial Key Laboratory for Cutting Tools Taizhou University Taizhou ChinaState Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing ChinaAbstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application.https://doi.org/10.1002/inf2.12028data processingdeep learningmachine learningmodelingvalidation |
| spellingShingle | Jing Wei Xuan Chu Xiang‐Yu Sun Kun Xu Hui‐Xiong Deng Jigen Chen Zhongming Wei Ming Lei Machine learning in materials science InfoMat data processing deep learning machine learning modeling validation |
| title | Machine learning in materials science |
| title_full | Machine learning in materials science |
| title_fullStr | Machine learning in materials science |
| title_full_unstemmed | Machine learning in materials science |
| title_short | Machine learning in materials science |
| title_sort | machine learning in materials science |
| topic | data processing deep learning machine learning modeling validation |
| url | https://doi.org/10.1002/inf2.12028 |
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