Learning dislocation dynamics mobility laws from large-scale MD
  simulations
Nicolas Bertin Vasily V. Bulatov Fei Zhou
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.