Accelerated search and design of stretchable graphene kirigami using machine learning

TL;DR

This paper demonstrates how machine learning, specifically convolutional neural networks, can rapidly predict and optimize kirigami cut patterns in graphene to achieve extreme stretchability, significantly reducing the search space and computational effort.

Contribution

The study introduces a CNN-based regression approach to efficiently explore the vast design space of kirigami patterns for stretchable materials, enabling rapid discovery of optimal designs.

Findings

CNN achieves near MD simulation accuracy in property prediction.

Only 1000 training samples needed for large design space.

ML accelerates the search for optimal kirigami patterns.

Abstract

Making kirigami-inspired cuts into a sheet has been shown to be an effective way of designing stretchable materials with metamorphic properties where the 2D shape can transform into complex 3D shapes. However, finding the optimal solutions is not straightforward as the number of possible cutting patterns grows exponentially with system size. Here, we report on how machine learning (ML) can be used to approximate the target properties, such as yield stress and yield strain, as a function of cutting pattern. Our approach enables the rapid discovery of kirigami designs that yield extreme stretchability as verified by molecular dynamics (MD) simulations. We find that convolutional neural networks (CNN), commonly used for classification in vision tasks, can be applied for regression to achieve an accuracy close to the precision of the MD simulations. This approach can then be used to search for optimal designs that maximize elastic stretchability with only 1000 training samples in a large design space of $\sim 4\times10^6$ candidate designs. This example demonstrates the power and potential of ML in finding optimal kirigami designs at a fraction of iterations that would be required of a purely MD or experiment-based approach, where no prior knowledge of the governing physics is known or available.