Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis
Abstract Achieving targeted microstructures through composition design is a core challenge in developing structural materials for high-performance applications. This study introduces a multiscale Integrated Computational Materials Engineering (ICME) framework that combines CALPHAD-based thermodynami...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01730-2 |
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| _version_ | 1849763975246381056 |
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| author | Jaemin Wang Hyeonseok Kwon Sang-Ho Oh Jae Heung Lee Dae Won Yun Hyungsoo Lee Seong-Moon Seo Young-Soo Yoo Hi Won Jeong Hyoung Seop Kim Byeong-Joo Lee |
| author_facet | Jaemin Wang Hyeonseok Kwon Sang-Ho Oh Jae Heung Lee Dae Won Yun Hyungsoo Lee Seong-Moon Seo Young-Soo Yoo Hi Won Jeong Hyoung Seop Kim Byeong-Joo Lee |
| author_sort | Jaemin Wang |
| collection | DOAJ |
| description | Abstract Achieving targeted microstructures through composition design is a core challenge in developing structural materials for high-performance applications. This study introduces a multiscale Integrated Computational Materials Engineering (ICME) framework that combines CALPHAD-based thermodynamic modeling, machine learning, molecular dynamics, and diffusion kinetics to link alloy chemistry to microstructural evolution. Machine learning models trained on 750,000 CALPHAD-derived datapoints enabled rapid screening of two billion compositions based on thermodynamic criteria. An advanced screening step incorporated nanoscale physical descriptors that capture mechanisms governing precipitate coarsening and dynamic recrystallization. Applied to wrought Ni-based superalloys, the framework identified twelve compositions predicted to form fine intragranular γ′ precipitates within coarse γ grains; one was experimentally validated, with microscopy confirming the predicted microstructure. While demonstrated for Ni-based systems, the methodology is broadly generalizable. This work highlights the power of integrating high-throughput composition screening with atomistic-scale evaluation to accelerate microstructure-driven materials design beyond equilibrium thermodynamics. |
| format | Article |
| id | doaj-art-ae864e0260374ef8bcf986617c26b5cd |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-ae864e0260374ef8bcf986617c26b5cd2025-08-20T03:05:15ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111210.1038/s41524-025-01730-2Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysisJaemin Wang0Hyeonseok Kwon1Sang-Ho Oh2Jae Heung Lee3Dae Won Yun4Hyungsoo Lee5Seong-Moon Seo6Young-Soo Yoo7Hi Won Jeong8Hyoung Seop Kim9Byeong-Joo Lee10Max Planck Institute for Sustainable MaterialsCenter for Advanced Aerospace Materials, Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)Extreme Materials Research Institute, Korea Institute of Materials ScienceExtreme Materials Research Institute, Korea Institute of Materials ScienceExtreme Materials Research Institute, Korea Institute of Materials ScienceExtreme Materials Research Institute, Korea Institute of Materials ScienceExtreme Materials Research Institute, Korea Institute of Materials ScienceGraduate Institute of Ferrous and Eco Materials Technology (GIFT), Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)Abstract Achieving targeted microstructures through composition design is a core challenge in developing structural materials for high-performance applications. This study introduces a multiscale Integrated Computational Materials Engineering (ICME) framework that combines CALPHAD-based thermodynamic modeling, machine learning, molecular dynamics, and diffusion kinetics to link alloy chemistry to microstructural evolution. Machine learning models trained on 750,000 CALPHAD-derived datapoints enabled rapid screening of two billion compositions based on thermodynamic criteria. An advanced screening step incorporated nanoscale physical descriptors that capture mechanisms governing precipitate coarsening and dynamic recrystallization. Applied to wrought Ni-based superalloys, the framework identified twelve compositions predicted to form fine intragranular γ′ precipitates within coarse γ grains; one was experimentally validated, with microscopy confirming the predicted microstructure. While demonstrated for Ni-based systems, the methodology is broadly generalizable. This work highlights the power of integrating high-throughput composition screening with atomistic-scale evaluation to accelerate microstructure-driven materials design beyond equilibrium thermodynamics.https://doi.org/10.1038/s41524-025-01730-2 |
| spellingShingle | Jaemin Wang Hyeonseok Kwon Sang-Ho Oh Jae Heung Lee Dae Won Yun Hyungsoo Lee Seong-Moon Seo Young-Soo Yoo Hi Won Jeong Hyoung Seop Kim Byeong-Joo Lee Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis npj Computational Materials |
| title | Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis |
| title_full | Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis |
| title_fullStr | Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis |
| title_full_unstemmed | Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis |
| title_short | Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis |
| title_sort | multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis |
| url | https://doi.org/10.1038/s41524-025-01730-2 |
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