What can machine learning help with microstructure-informed materials modeling and design?
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913367183065088 |
|---|---|
| author | Peng, Xiang-Long Fathidoost, Mozhdeh Lin, Binbin Yang, Yangyiwei Xu, Bai-Xiang |
| author_facet | Peng, Xiang-Long Fathidoost, Mozhdeh Lin, Binbin Yang, Yangyiwei Xu, Bai-Xiang |
| contents | Machine learning 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 machine learning-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 machine learning, microstructure descriptors and machine learning-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of machine learning-based modeling and design of microstructured materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18396 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What can machine learning help with microstructure-informed materials modeling and design? Peng, Xiang-Long Fathidoost, Mozhdeh Lin, Binbin Yang, Yangyiwei Xu, Bai-Xiang Materials Science Computational Physics Machine learning 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 machine learning-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 machine learning, microstructure descriptors and machine learning-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of machine learning-based modeling and design of microstructured materials. |
| title | What can machine learning help with microstructure-informed materials modeling and design? |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2405.18396 |