Supervised Autoencoder for Inverse Kirigami Design

Recently, machine learning (ML) methods have shown successes in predicting mechanical properties of composite materials as a forward solver. While ML approach is much faster than the conventional physics-based solvers (e.g. molecular dynamics), most current ML techniques need to screen the entire library in order to perform inverse design. Thus, this approach might no longer be practical for a system with a very large design space. Here, we use a supervised-autoencoder (sAE) to perform inverse design in graphene kirigami where predicting ultimate stress or fracture point is known to be difficult due to nonlinear effects arise from the out-of-plane buckling. Unlike the standard autoencoder, our sAE is not only able to reconstruct cut configurations but also to predict mechanical properties of graphene kirigami and classify graphene kirigami with either parallel or orthogonal cuts. Furthermore, we find that by interpolating different configurations the sAE is able to generate new designs consisting of mixed parallel and orthogonal cuts while only being trained with kirigami structures with parallel and orthogonal cuts. This allows us to design and optimize materials in the latent space, which is more efficient than to perform optimization in the original representation.