Forward and Inverse Design of Kirigami via Supervised Autoencoder

TL;DR

This paper introduces a supervised autoencoder that enables inverse design and property prediction of graphene kirigami structures, facilitating the discovery of novel configurations with desired mechanical properties.

Contribution

The study develops a supervised autoencoder capable of reconstructing kirigami configurations, predicting their mechanical properties, and generating new designs by interpolating in latent space.

Findings

The sAE accurately predicts stress and strain of kirigami structures.

It can generate novel kirigami designs by interpolating in latent space.

The method classifies kirigami cuts as parallel or orthogonal.

Abstract

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 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 mechanical properties of graphene kirigami and classify the kirigami witheither parallel or orthogonal cuts. By interpolating in the latent space of kirigami structures, the sAE is able to generate novel designs that mix parallel and orthogonal cuts, despite being trained independently on parallel or orthogonal cuts. Our method allows us to both identify novel designs and predict, with reasonable accuracy, their mechanical properties, which is crucial for expanding the search space for materials design.