  Graphene is one of the most researched two dimensional (2D) material due to
its unique combination of mechanical, thermal and electrical properties.
Special 2D structure of graphene enables it to exhibit a wide range of peculiar
material properties like high Young's modulus, high specific strength etc.
which are critical for myriad of applications including light weight structural
materials, multi-functional coating and flexible electronics. It is quite
challenging and costly to experimentally investigate graphene/graphene based
nanocomposites, computational simulations such as molecular dynamics (MD)
simulations are widely adopted for understanding the microscopic origins of
their unique properties. However, disparate results were reported from
computational studies, especially MD simulations using various empirical
inter-atomic potentials. In this work, an artificial neural network based
interatomic potential has been developed for graphene to represent the
potential energy surface based on first principle calculations. The developed
machine learning potential (MLP) facilitates high fidelity MD simulations to
approach the accuracy of ab initio methods but with a fraction of computational
cost, which allows larger simulation size/length, and thereby enables
accelerated discovery/design of graphene-based novel materials. Lattice
parameter, coefficient of thermal expansion (CTE), Young's modulus and yield
strength are estimated using machine learning accelerated MD simulations
(MLMD), which are compared to experimental/first principle calculations from
previous literatures. It is demonstrated that MLMD can capture the dominating
mechanism governing CTE of graphene, including effects from lattice parameter
and out of plane rippling.
