Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors
In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystallin...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Science and Technology of Advanced Materials |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/14686996.2024.2436347 |
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| _version_ | 1832592171868356608 |
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| author | Akiyasu Yamamoto Akinori Yamanaka Kazumasa Iida Yusuke Shimada Satoshi Hata |
| author_facet | Akiyasu Yamamoto Akinori Yamanaka Kazumasa Iida Yusuke Shimada Satoshi Hata |
| author_sort | Akiyasu Yamamoto |
| collection | DOAJ |
| description | In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e. grains, grain boundaries [GBs], and microstructures) and summarize their various aspects (experimental synthesis, artificial single GBs, multiscale experimental data acquisition via electron microscopy, formation process modeling, property description modeling, 3D reconstruction, and data-driven design methods). Specifically, we discuss a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using BOXVIA software. As a proof of concept, a researcher- and data-driven process design methodology is applied to a polycrystalline iron-based superconductor to evaluate its bulk magnet properties. Finally, future challenges and prospects for data-driven material development and iron-based superconductors are discussed. |
| format | Article |
| id | doaj-art-c2f77d50659b484fb1d0499338f5977b |
| institution | Kabale University |
| issn | 1468-6996 1878-5514 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials |
| spelling | doaj-art-c2f77d50659b484fb1d0499338f5977b2025-01-21T14:09:48ZengTaylor & Francis GroupScience and Technology of Advanced Materials1468-69961878-55142025-12-0126110.1080/14686996.2024.2436347Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductorsAkiyasu Yamamoto0Akinori Yamanaka1Kazumasa Iida2Yusuke Shimada3Satoshi Hata4Department of Applied Physics, Tokyo University of Agriculture and Technology, Tokyo, JapanJST-CREST, Saitama, JapanJST-CREST, Saitama, JapanJST-CREST, Saitama, JapanJST-CREST, Saitama, JapanIn this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e. grains, grain boundaries [GBs], and microstructures) and summarize their various aspects (experimental synthesis, artificial single GBs, multiscale experimental data acquisition via electron microscopy, formation process modeling, property description modeling, 3D reconstruction, and data-driven design methods). Specifically, we discuss a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using BOXVIA software. As a proof of concept, a researcher- and data-driven process design methodology is applied to a polycrystalline iron-based superconductor to evaluate its bulk magnet properties. Finally, future challenges and prospects for data-driven material development and iron-based superconductors are discussed.https://www.tandfonline.com/doi/10.1080/14686996.2024.2436347Bayesian optimizationBOXVIAdata assimilationdata-driven process designdeep learningDFT |
| spellingShingle | Akiyasu Yamamoto Akinori Yamanaka Kazumasa Iida Yusuke Shimada Satoshi Hata Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors Science and Technology of Advanced Materials Bayesian optimization BOXVIA data assimilation data-driven process design deep learning DFT |
| title | Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors |
| title_full | Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors |
| title_fullStr | Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors |
| title_full_unstemmed | Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors |
| title_short | Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors |
| title_sort | integrating machine learning with advanced processing and characterization for polycrystalline materials a methodology review and application to iron based superconductors |
| topic | Bayesian optimization BOXVIA data assimilation data-driven process design deep learning DFT |
| url | https://www.tandfonline.com/doi/10.1080/14686996.2024.2436347 |
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