Search and design of stretchable graphene kirigami using convolutional neural networks

Making kirigami-inspired cuts in a sheet has been shown to be an effective way to design stretchable materials with metamorphic properties where the 2D shape can transform into complex 3D shapes. However, a systematic understanding on how cutting patterns alter the mechanical properties of the resulting kirigami remains elusive. Here, we use machine learning (ML) to approximate the objective functions, such as yield stress and yield strain, as functions of the cutting pattern[1]. Our approach enables the rapid discovery of graphene kirigami designs that yield extreme stretchability as verified by molecular dynamics (MD) simulations. We find that convolutional neural networks (CNN) 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 roughly 4,000,000 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.

Reference: arXiv preprint arXiv:1808.06111