论文标题
使用图神经网络加速离散的错位动力学模拟
Accelerating discrete dislocation dynamics simulations with graph neural networks
论文作者
论文摘要
离散脱位动力学（DDD）是一种广泛使用的计算方法，用于研究中尺度上的可塑性，该方法将脱位线的运动与晶体材料的宏观响应联系起来。但是，DDD模拟的计算成本仍然是限制其适用性范围的瓶颈。在这里，我们介绍了一个新的DDD-GNN框架，其中昂贵的位错运动的时间整合完全由训练DDD轨迹的图形神经网络（GNN）模型代替。作为第一个应用程序，我们在简单但相关的位错线模型上滑行一系列障碍物的简单但相关的模型，证明了我们方法的可行性和潜力。我们表明，DDD-GNN模型是稳定的，并且对一系列紧张的速率和障碍物密度的重现，无需在时间整合过程中明确计算淋巴结或脱位迁移率。我们的方法开放了新的有前途的途径，以加速DDD模拟并结合更复杂的脱位运动行为。
Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the computational cost of DDD simulations remains a bottleneck that limits its range of applicability. Here, we introduce a new DDD-GNN framework in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (GNN) model trained on DDD trajectories. As a first application, we demonstrate the feasibility and potential of our method on a simple yet relevant model of a dislocation line gliding through an array of obstacles. We show that the DDD-GNN model is stable and reproduces very well unseen ground-truth DDD simulation responses for a range of straining rates and obstacle densities, without the need to explicitly compute nodal forces or dislocation mobilities during time-integration. Our approach opens new promising avenues to accelerate DDD simulations and to incorporate more complex dislocation motion behaviors.
