Studies of Shape Memory Graphene Nanostructures via Integration of Physics-based Modelling and Machine Learning

Abstract

In this contribution, we study the properties of new promising graphenebased materials with shape memory effects. While traditional shape memory alloys have been extensively studied, it is a challenge to preserve shape memory properties at the nanoscale. As a result, new materials have been explored, among which graphene oxide (GO) crystals with ordered epoxy groups where a recoverable strain of 14.5% has already been reported. We use such nanoscale GO structures as a benchmark example for our studies here. MBTR and SOAP representations are employed in a general-purpose ML model to analyze the effect of long-range interactions in GO. Finally, a physics-based ML model allows us to build interatomic potentials for 2D and 1D systems. The model predicts quantum mechanical effects due to the electronic confinement in narrow nanoribbons and shows the evolution of the local minimal energies associated with two-phase states.

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