A Graph Neural Network Approach to Dislocation Dynamics

Accelerating discrete dislocation dynamics simulations with graph neural networks

Discrete dislocation dynamics (ddd) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocationlines 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 approach in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (gnn) model trained on ddd trajectories.We show that the 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 newpromising avenues to accelerate ddd simulations and to incorporate more complex dislocation motion behaviors.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 a forest of obstacles.