论文标题
热梯度耦合石墨烯断裂的多尺度力学:一项分子动力学研究
Multiscale mechanics of thermal gradient coupled graphene fracture: A molecular dynamics study
论文作者
论文摘要
在热梯度下,石墨烯断裂的热机械耦合机制具有丰富的应用,而由于其耦合的非平衡性质,很难研究。我们采用非平衡分子动力学来研究石墨烯的断裂,通过使用不同的潜在电场在不同的热梯度下施加固定应变速率。发现对于Airebo和Airebo-M,断裂应力并不严格遵循与初始裂纹长度的正相关。对于小初始缺陷的“基于REBO”的潜在模型,观察到应变硬化效应,这被解释为对多孔石墨烯观察到的钝效应。观察到温度梯度不会显示与断裂应力和裂纹传播动力学的明显关系。量化断裂力学验证我们的分子动力学计算。我们提供了一个独特的观点,即横向键作用共享负载,以说明断裂应力的非线性增加,裂纹长度较短。对于“基于REBO的”潜在模型,观察到沿裂纹尖端的异常动能传输,我们将其归因于潜在模型中高原子间吸引。裂缝很荣幸使用机器学习间潜能(MLIP)进行更“脆性”,但无法模拟骨折后动态行为。观察到使用MLIP的机械响应与温度梯度无关。据报道,使用辍学不确定性神经网络电位的平衡模拟的温度构型据报道,辍学率为0.1是最准确的。预计这项工作将激发对石墨烯中非平衡动力学的进一步研究,并在各个工程领域使用实际应用。
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.