Research on Multiscale Characterization and Computational Modeling/Simulation of Metallic Materials.
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| Title: | Research on Multiscale Characterization and Computational Modeling/Simulation of Metallic Materials. |
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| Authors: | Wang, Rui1 (AUTHOR), Li, Jiaqing2 (AUTHOR) jiaqing@fzu.edu.cn |
| Source: | Materials (1996-1944). Apr2026, Vol. 19 Issue 7, p1417. 5p. |
| Subjects: | Multiple scale method, Metals, Finite element method, Computer simulation, Machine learning, Materials science, Artificial intelligence, Molecular dynamics |
| Abstract: | This article focuses on recent advances in the multiscale characterization and computational modeling/simulation of metallic materials, emphasizing their importance for strategic sectors like aerospace and energy. It highlights the integration of experimental techniques across atomic, mesoscale, and macroscopic levels with computational methods, including molecular dynamics and finite element simulations, to better understand microstructural evolution and mechanical performance. The article also discusses the emerging role of artificial intelligence and machine learning in predicting material properties and accelerating materials development. Future directions include enhancing in situ characterization methods and combining physics-based models with data-driven approaches to improve the accuracy and efficiency of metallic materials research. [Extracted from the article] |
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| Database: | Engineering Source |
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| Abstract: | This article focuses on recent advances in the multiscale characterization and computational modeling/simulation of metallic materials, emphasizing their importance for strategic sectors like aerospace and energy. It highlights the integration of experimental techniques across atomic, mesoscale, and macroscopic levels with computational methods, including molecular dynamics and finite element simulations, to better understand microstructural evolution and mechanical performance. The article also discusses the emerging role of artificial intelligence and machine learning in predicting material properties and accelerating materials development. Future directions include enhancing in situ characterization methods and combining physics-based models with data-driven approaches to improve the accuracy and efficiency of metallic materials research. [Extracted from the article] |
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| ISSN: | 19961944 |
| DOI: | 10.3390/ma19071417 |
