Forward and Inverse Design of Kirigami via Supervised Autoencoder

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 with either 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.

Learning crystal field parameters using convolutional neural networks

“…Motivated by the multitude of recent explorations of machine-learning (ML) techniques in physics [27][28][29], in general, and the success of artificial neural networks and other ML approaches to attack complex inverse problems of physics [30][31][32][33][34], in particular, we here study how ML can be used to extract Stevens CF parameters from thermodynamic measurements. This data-driven approach to inverse problems is based on first computing a large set of training data {(p j , F P (p j ))|j = 1, 2, .…”