Uncovering stress fields and defects distributions in graphene using deep neural networks

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.