Small data machine learning in materials science
This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction...
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| Published in | npj computational materials Vol. 9; no. 1; pp. 42 - 15 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
25.03.2023
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed. |
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| AbstractList | This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed. Abstract This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed. |
| ArticleNumber | 42 |
| Author | Ji, Xiaobo Lu, Wencong Xu, Pengcheng Li, Minjie |
| Author_xml | – sequence: 1 givenname: Pengcheng surname: Xu fullname: Xu, Pengcheng organization: Materials Genome Institute, Shanghai University – sequence: 2 givenname: Xiaobo surname: Ji fullname: Ji, Xiaobo organization: Department of Chemistry, College of Sciences, Shanghai University – sequence: 3 givenname: Minjie orcidid: 0000-0001-5048-6211 surname: Li fullname: Li, Minjie email: minjieli@shu.edu.cn organization: Department of Chemistry, College of Sciences, Shanghai University – sequence: 4 givenname: Wencong orcidid: 0000-0001-5361-6122 surname: Lu fullname: Lu, Wencong email: wclu@shu.edu.cn organization: Materials Genome Institute, Shanghai University, Department of Chemistry, College of Sciences, Shanghai University, Zhejiang Laboratory |
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| Snippet | This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the... Abstract This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then,... |
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| SubjectTerms | 639/301/1034 639/301/1034/1037 Active learning Algorithms Artificial intelligence Big Data Characterization and Evaluation of Materials Chemistry and Materials Science Computational Intelligence Data collection Experiments Genomes Interdisciplinary subjects Learning algorithms Machine learning Materials Science Mathematical and Computational Engineering Mathematical and Computational Physics Mathematical Modeling and Industrial Mathematics Review Article Theoretical Transfer learning Workflow |
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| Title | Small data machine learning in materials science |
| URI | https://link.springer.com/article/10.1038/s41524-023-01000-z https://www.proquest.com/docview/2790694538 https://doaj.org/article/276148dc3de9420294a1b388350f6c64 |
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