Learning dislocation dynamics mobility laws from large-scale MD simulations

TL;DR

This paper presents a machine learning framework using graph neural networks trained on large-scale MD simulations to develop accurate, data-driven mobility laws for dislocation dynamics, improving the fidelity and efficiency of metal plasticity modeling.

Contribution

It introduces a novel ML-based approach to derive dislocation mobility laws from MD data, enabling automated, accurate, and faster dislocation dynamics simulations.

Findings

GNN mobility laws reproduce MD-observed tension/compression asymmetry

Accurately predict flow stress at unseen lower strain rates

Significantly faster than direct MD simulations

Abstract

The computational method of discrete dislocation dynamics (DDD), used as a coarse-grained model of true atomistic dynamics of lattice dislocations, has become of powerful tool to study metal plasticity arising from the collective behavior of dislocations. As a mesoscale approach, motion of dislocations in the DDD model is prescribed via the mobility law; a function which specifies how dislocation lines should respond to the driving force. However, the development of traditional hand-crafted mobility laws can be a cumbersome task and may involve detrimental simplifications. Here we introduce a machine-learning (ML) framework to streamline the development of data-driven mobility laws which are modeled as graph neural networks (GNN) trained on large-scale Molecular Dynamics (MD) simulations of crystal plasticity. We illustrate our approach on BCC tungsten and demonstrate that our GNN mobility implemented in large-scale DDD simulations accurately reproduces the challenging tension/compression asymmetry observed in ground-truth MD simulations while correctly predicting the flow stress at lower straining rate conditions unseen during training, thereby demonstrating the ability of our method to learn relevant dislocation physics. Our DDD+ML approach opens new promising avenues to improve fidelity of the DDD model and to incorporate more complex dislocation motion behaviors in an automated way, providing a faithful proxy for dislocation dynamics several orders of magnitude faster than ground-truth MD simulations.