Peng, Xiang-Long ; Fathidoost, Mozhdeh ; Lin, Binbin ; Yang, Yangyiwei ; Xu, Bai-Xiang (2024)
What can machine learning help with microstructure-informed materials modeling and design?
In: MRS Bulletin
doi: 10.1557/s43577-024-00797-4
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Machine learning (ML) techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current ML-assisted and data-driven advancements in this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on ML, microstructure descriptors, and ML-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of ML-based modeling and design of microstructured materials. Graphical abstract
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Peng, Xiang-Long ; Fathidoost, Mozhdeh ; Lin, Binbin ; Yang, Yangyiwei ; Xu, Bai-Xiang |
| Art des Eintrags: | Bibliographie |
| Titel: | What can machine learning help with microstructure-informed materials modeling and design? |
| Sprache: | Englisch |
| Publikationsjahr: | 26 Oktober 2024 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | MRS Bulletin |
| DOI: | 10.1557/s43577-024-00797-4 |
| Kurzbeschreibung (Abstract): | Machine learning (ML) techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current ML-assisted and data-driven advancements in this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on ML, microstructure descriptors, and ML-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of ML-based modeling and design of microstructured materials. Graphical abstract |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Mechanik Funktionaler Materialien Zentrale Einrichtungen Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner |
| Hinterlegungsdatum: | 21 Nov 2024 06:12 |
| Letzte Änderung: | 21 Nov 2024 06:47 |
| PPN: | 52369153X |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
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