Machine learning (ML) methods have recently been used as forward solvers to predict the mechanical properties of composite materials. Here, we use a supervised autoencoder (SAE) to perform the inverse design of graphene kirigami, where predicting the ultimate stress or strain under tensile loading is known to be difficult due to nonlinear effects arising from the out-of-plane buckling. Unlike the standard autoencoder, our SAE is able not only to reconstruct cut configurations but also to predict the mechanical properties of graphene kirigami and classify the kirigami with either parallel or orthogonal cuts. By interpolating in the latent space of kirigami structures, the SAE is able to generate designs that mix parallel and orthogonal cuts, despite being trained independently on parallel or orthogonal cuts. Our method allows us to both identify alternate designs and predict, with reasonable accuracy, their mechanical properties, which is crucial for expanding the search space for materials design.

• Received 28 May 2020
• Accepted 28 September 2020

DOI:https://doi.org/10.1103/PhysRevResearch.2.042006

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society