Reliable machine learning potentials based on artificial neural network for graphene

A Singh and YM Li, COMPUTATIONAL MATERIALS SCIENCE, 227, 112272 (2023).

DOI: 10.1016/j.commatsci.2023.112272

Graphene is one of the most researched two dimensional (2D) material in the past two decades 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, electrical conductivity etc. which are critical for myriad of applications including lightweight structural materials, multi-functional coating and flexible electronics. As it is quite challenging and costly to experimentally investigate graphene and 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 (ANN) based interatomic force field 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 and length, and thereby enables accelerated discovery and 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 and first principle calculations from previous literatures. It is demonstrated that MLMD can capture the dominating mechanism governing the CTE of graphene, including effects from lattice parameter and out of plane rippling. The MLMD approach is highly scalable for 2D materials and can help in accelerating the research of novel 2D materials and 2D material hybrids with unique atomic structures.

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