Deep learning model to predict fracture mechanisms of graphene
Source abstract
Abstract
Abstract 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.
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Research context
OpenAlex topics: Graphene research and applications · Machine Learning in Materials Science · Nanopore and Nanochannel Transport Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (38)
Show source-provided references
1. Buehler, M. J. Atomistic Modeling of Materials Failure, https://doi.org/10.1007/978-0-387-76426-9 (Springer, 2008).10.1007/978-0-387-76426-9 ↗
2. Jung, G. S. et al. Interlocking friction governs the mechanical fracture of bilayer MoS2. ACS Nano 12, 3600–3608 (2018).10.1021/acsnano.8b00712 ↗
3. Jung, G. S. et al. Anisotropic fracture dynamics due to local lattice distortions. ACS Nano 13, 5693–5702 (2019).10.1021/acsnano.9b01071 ↗
4. Wang, S. S. et al. Atomically sharp crack tips in monolayer MoS2 and their enhanced toughness by vacancy defects. ACS Nano 10, 9831–9839 (2016).10.1021/acsnano.6b05435 ↗
5. Peng, G. C. Y. et al. Multiscale modeling meets machine learning: what can we learn? Arch. Comput. Methods Eng. 1, 3 (2020).10.1007/s11831-020-09405-5 ↗
6. Tong, Q. & Li, S. A concurrent multiscale study of dynamic fracture. Comput. Methods Appl. Mech. Eng. 366, 113075 (2020).10.1016/j.cma.2020.113075 ↗
7. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).10.1038/nature14539 ↗
8. Udrescu, S.-M. & Tegmark, M. Symbolic pregression: discovering physical laws from raw distorted video. arXiv 2005.11212 (2020).10.1103/PhysRevE.103.043307 ↗
9. Jackson, N. E., Webb, M. A. & de Pablo, J. J. Recent advances in machine learning towards multiscale soft materials design. Curr. Opin. Chem. Eng. 23, 106–114 (2019).10.1016/j.coche.2019.03.005 ↗
10. Xu, J. Distance-based protein folding powered by deep learning. bioRxiv 465955, https://doi.org/10.1101/465955 (2018).10.1101/465955 ↗
11. AlQuraishi, M. End-to-end differentiable learning of protein structure. Cell Syst. 8, 292–301 (2019).10.1016/j.cels.2019.03.006 ↗
12. Yu, C.-H. & Buehler, M. J. Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling. APL Bioeng. 4, 016108 (2020).10.1063/1.5133026 ↗
13. Wang, J., Cao, H., Zhang, J. Z. H. & Qi, Y. Computational protein design with deep learning neural networks. Sci. Rep. 8, 6349 (2018).10.1038/s41598-018-24760-x ↗
14. Yu, C.-H., Qin, Z., Martin-Martinez, F. J. & Buehler, M. J. A self-consistent sonification method to translate amino acid sequences into musical compositions and application in protein design using artificial intelligence. ACS Nano 13, 7471–7482 (2019).10.1021/acsnano.9b02180 ↗
15. Qin, Z. et al. Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence. Extrem. Mech. Lett. 36, 100652 (2020).10.1016/j.eml.2020.100652 ↗
16. Gu, G. X., Chen, C.-T., Richmond, D. J. & Buehler, M. J. Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater. Horiz. 5, 939–945 (2018).10.1039/C8MH00653A ↗
17. Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R. & Tkatchenko, A. Quantum-chemical insights from deep tensor neural networks. Nat. Commun. 8, 6–13 (2017).10.1038/s41467-016-0015-8 ↗
18. Yu, C. H., Qin, Z., Martin-Martinez, F. & Buehler, M. J. A self-consistent sonification method to translate amino acid sequences into musical compositions and application in protein design using AI. ACS Nano 13, 7471–7482 (2019).10.1021/acsnano.9b02180 ↗
19. Dewapriya, M. A. N., Rajapakse, R. K. N. D. & Dias, W. P. S. Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks. Carbon 163, 425–440 (2020).10.1016/j.carbon.2020.03.038 ↗
20. Hsu, Y. C., Yu, C. H. & Buehler, M. J. Using deep learning to predict fracture patterns in crystalline solids. Matter 3, 197–211 (2020).10.1016/j.matt.2020.04.019 ↗
21. Sen, D. & Buehler, M. J. Chemical complexity in mechanical deformation of metals. Int. J. Multiscale Comput. Eng. 5, 181–202 (2007).10.1615/IntJMultCompEng.v5.i3-4.30 ↗
22. Qin, Z. & Buehler, M. J. Nonlinear viscous water at nanoporous two-dimensional interfaces resists high-speed flow through cooperativity. Nano Lett. 15, 3939–3944 (2015).10.1021/acs.nanolett.5b00809 ↗
23. Cranford, S. W. & Buehler, M. J. Mechanical properties of graphyne. Carbon 49, 4111–4121 (2011).10.1016/j.carbon.2011.05.024 ↗
24. Liu, F., Ming, P. & Li, J. Ab initio calculation of ideal strength and phonon instability of graphene under tension. Phys. Rev. B - Condens. Matter Mater. Phys. 76, 064120 (2007).10.1103/PhysRevB.76.064120 ↗
25. Chu, Y., Ragab, T. & Basaran, C. The size effect in mechanical properties of finite-sized graphene nanoribbon. Comput. Mater. Sci. 81, 269–274 (2014).10.1016/j.commatsci.2013.08.016 ↗
26. Jung, G. S., Qin, Z. & Buehler, M. J. Molecular mechanics of polycrystalline graphene with enhanced fracture toughness. Extrem. Mech. Lett. 2, 52–59 (2015).10.1016/j.eml.2015.01.007 ↗
27. Zhao, H., Min, K. & Aluru, N. R. Size and chirality dependent elastic properties of graphene nanoribbons under uniaxial tension. Nano Lett. 9, 3012–3015 (2009).10.1021/nl901448z ↗
28. Mandelbrot, B. B. The Fractal Geometry of Nature (W. H. Freeman, 1983).10.1119/1.13295 ↗
29. Zhang, T., Li, X., Kadkhodaei, S. & Gao, H. Flaw insensitive fracture in nanocrystalline graphene. Nano Lett. 12, 4605–4610 (2012).10.1021/nl301908b ↗
30. Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).10.1006/jcph.1995.1039 ↗
31. Stuart, S. J., Tutein, A. B. & Harrison, J. A. A reactive potential for hydrocarbons with intermolecular interactions. J. Chem. Phys. 112, 6472–6486 (2000).10.1063/1.481208 ↗
32. Liu, Y., Dobrinsky, A. & Yakobson, B. I. Graphene edge from armchair to zigzag: the origins of nanotube chirality? Phys. Rev. Lett. https://doi.org/10.1103/PhysRevLett.105.235502 (2010).10.1103/PhysRevLett.105.235502 ↗
33. Nosé, S. A molecular dynamics method for simulations in the canonical ensemble. Mol. Phys. 52, 255–268 (1984).10.1080/00268978400101201 ↗
34. Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO-the Open Visualization Tool. Model. Simul. Mater. Sci. Eng. 18, 015012 (2010).10.1088/0965-0393/18/1/015012 ↗
35. LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2323 (1998).10.1109/5.726791 ↗
36. Oore, S., Simon, I., Dieleman, S., Eck, D. & Simonyan, K. This time with feeling: learning expressive musical performance. Neural Comput. Appl. 32, 955–967 (2020).10.1007/s00521-018-3758-9 ↗
37. Falconer, K. J. The Hausdorff dimension of self-affine fractals. Math. Proc. Camb. Philos. Soc. 103, 339–350 (1988).10.1017/S0305004100064926 ↗
38. Costa, A. Hausdorff (box-counting) fractal dimension. MATLAB Central File Exchange, http://mathworks.com/matlabcentral/fileexchange/30329-hausdorff--box-counting--fractal-dimension (2013).