Accelerating discrete dislocation dynamics simulations with graph neural networks

TL;DR

This paper introduces a graph neural network framework that replaces traditional time-integration in discrete dislocation dynamics simulations, significantly reducing computational costs while maintaining accuracy.

Contribution

The authors develop a novel DDD-GNN framework that accelerates dislocation dynamics simulations by substituting explicit force calculations with a trained neural network.

Findings

The DDD-GNN model accurately reproduces unseen DDD responses.

The approach is stable across various strain rates and obstacle densities.

It eliminates the need for explicit force computations during simulations.

Abstract

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.