Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors Article Swipe
Akiyasu Yamamoto
,
Akinori Yamanaka
,
K. Iida
,
Yusuke Shimada
,
Satoshi Hata
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/14686996.2024.2436347
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/14686996.2024.2436347
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.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1080/14686996.2024.2436347
- OA Status
- gold
- Cited By
- 4
- References
- 362
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1080/14686996.2024.2436347Digital Object Identifier
- Title
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Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductorsWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-16Full publication date if available
- Authors
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Akiyasu Yamamoto, Akinori Yamanaka, K. Iida, Yusuke Shimada, Satoshi HataList of authors in order
- Landing page
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https://doi.org/10.1080/14686996.2024.2436347Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1080/14686996.2024.2436347Direct OA link when available
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Crystallite, Materials science, Computer science, Characterization (materials science), Artificial intelligence, Nanotechnology, Machine learning, MetallurgyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5055256393 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210086780, https://openalex.org/I92614990 |
| citation_normalized_percentile.value | 0.77743257 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |