Abstract
Deep learning provides a new route for developing computationally efficient predictive models for some complex engineering problems by eliminating the need for establishing exact governing equations. In this work, we used conditional generative adversarial networks (cGANs) to identify defects in graphene samples and to predict the complex stress fields created by two interacting defective regions in graphene. The required data for developing deep learning models was obtained from molecular dynamics simulations, where the numerical results of the simulations were transformed into image-based data. Our results demonstrate that the neural nets can accurately predict some complex features of the interacting stress fields. Subsequently, we used cGANs to predict defect distributions; this revealed that a cGAN could predict the existence of a crack even though it had never seen a cracked sample during the training stage. This observation clearly demonstrates the remarkable generalizability of cGANs beyond the training samples, suggesting that deep learning can be a powerful tool for solving advanced nanoengineering problems.
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Supporting information contains three additional figures. The trained neural networks, complete data set, and MATLAB script used to generate molecular dynamics simulation files are freely available here: https://doi.org/10.5281/zenodo.7834444.
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Acknowledgements
This research was supported by Natural Sciences and Engineering Research Council of Canada. Computing resources for the simulations were provided by Compute Ontario and Compute Canada.
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The problem was jointly identified by the three authors. MAND did the formulation, and all computations and analyzed the results. RKNDR and WPSD reviewed the results and suggested improvements. MAND prepared the draft manuscript and RKNDR and WPSD reviewed the draft and provided comments. All authors reviewed and revised the final manuscript.
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Dewapriya, M.A.N., Rajapakse, R.K.N.D. & Dias, W.P.S. Uncovering stress fields and defects distributions in graphene using deep neural networks. Int J Fract 242, 107–127 (2023). https://doi.org/10.1007/s10704-023-00704-z
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DOI: https://doi.org/10.1007/s10704-023-00704-z