Accelerated search and design for stretchable graphene kirigami using machine learning
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
classical 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
data 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.
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
classical 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
data 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.
Research Areas
