Multiscale mechanics of thermal gradient coupled graphene fracture: A molecular dynamics study

TL;DR

This study uses molecular dynamics to explore how thermal gradients influence graphene fracture mechanics, revealing complex behaviors and the effects of different interatomic potentials on fracture responses.

Contribution

It provides new insights into the thermo-mechanical coupling in graphene fracture and evaluates the impact of various potential models, including machine learning potentials, on fracture simulation accuracy.

Findings

Fracture stresses do not always positively correlate with initial crack length.

Strain-hardening effects observed for small defects with REBO-based potentials.

Temperature gradients show no clear relation to fracture stresses or crack dynamics.

Abstract

The thermo-mechanical coupling mechanism of graphene fracture under thermal gradients possesses rich applications whereas is hard to study due to its coupled non-equilibrium nature. We employ non-equilibrium molecular dynamics to study the fracture of graphene by applying a fixed strain rate under different thermal gradients by employing different potential fields. It is found that for AIREBO and AIREBO-M, the fracture stresses do not strictly follow the positive correlations with the initial crack length. Strain-hardening effects are observed for "REBO-based" potential models of small initial defects, which is interpreted as blunting effect observed for porous graphene. The temperature gradients are observed to not show clear relations with the fracture stresses and crack propagation dynamics. Quantized fracture mechanics verifies our molecular dynamics calculations. We provide a unique perspective that the transverse bond forces share the loading to account for the nonlinear increase of fracture stress with shorter crack length. Anomalous kinetic energy transportation along crack tips is observed for "REBO-based" potential models, which we attribute to the high interatomic attractions in the potential models. The fractures are honored to be more "brittle-liked" carried out using machine learning interatomic potential (MLIP), yet incapable of simulating post-fracture dynamical behaviors. The mechanical responses using MLIP are observed to be not related to temperature gradients. The temperature configuration of equilibration simulation employing the dropout uncertainty neural network potential with a dropout rate of 0.1 is reported to be the most accurate compared with the rest. This work is expected to inspire further investigation of non-equilibrium dynamics in graphene with practical applications in various engineering fields.