Deep learning model to predict fracture mechanisms of graphene
Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fr...
| Main Authors: | , , , |
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| Format: | Article |
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Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/132662 |
| _version_ | 1826193355726389248 |
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| author | Lew, Andrew James Yu, Chi-Hua Hsu, Yu-Chuan Buehler, Markus J |
| author2 | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics |
| author_facet | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics Lew, Andrew James Yu, Chi-Hua Hsu, Yu-Chuan Buehler, Markus J |
| author_sort | Lew, Andrew James |
| collection | MIT |
| description | Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials. |
| first_indexed | 2024-09-23T09:37:45Z |
| format | Article |
| id | mit-1721.1/132662 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:37:45Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1326622022-09-30T15:48:01Z Deep learning model to predict fracture mechanisms of graphene Lew, Andrew James Yu, Chi-Hua Hsu, Yu-Chuan Buehler, Markus J Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics Massachusetts Institute of Technology. Department of Chemistry Massachusetts Institute of Technology. Center for Computational Science and Engineering Massachusetts Institute of Technology. Center for Materials Science and Engineering Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials. NSF (Grant 1122374) Office of Naval Research (Grants N000141612333 and N000141912375) AFOSR-MURI (Contract FA9550-15-1-0514) Army Research Office (Contract W911NF1920098) NIH (Grant U01-EB014976) 2021-09-29T17:41:35Z 2021-09-29T17:41:35Z 2021-04 2020-09 Article http://purl.org/eprint/type/JournalArticle 2397-7132 https://hdl.handle.net/1721.1/132662 Lew, Andrew J. et al. "Deep learning model to predict fracture mechanisms of graphene." npj 2D Materials and Applications 5, 1 (April 2021): 48. © 2021 The Author(s) https://doi.org/10.1038/s41699-021-00228-x npj 2D Materials and Applications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Lew, Andrew James Yu, Chi-Hua Hsu, Yu-Chuan Buehler, Markus J Deep learning model to predict fracture mechanisms of graphene |
| title | Deep learning model to predict fracture mechanisms of graphene |
| title_full | Deep learning model to predict fracture mechanisms of graphene |
| title_fullStr | Deep learning model to predict fracture mechanisms of graphene |
| title_full_unstemmed | Deep learning model to predict fracture mechanisms of graphene |
| title_short | Deep learning model to predict fracture mechanisms of graphene |
| title_sort | deep learning model to predict fracture mechanisms of graphene |
| url | https://hdl.handle.net/1721.1/132662 |
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