  Dislocation mobility, which dictates the response of dislocations to an
applied stress, is a fundamental property of crystalline materials that governs
the evolution of plastic deformation. Traditional approaches for deriving
mobility laws rely on phenomenological models of the underlying physics, whose
free parameters are in turn fitted to a small number of intuition-driven atomic
scale simulations under varying conditions of temperature and stress. This
tedious and time-consuming approach becomes particularly cumbersome for
materials with complex dependencies on stress, temperature, and local
environment, such as body-centered cubic crystals (BCC) metals and alloys. In
this paper, we present a novel, uncertainty quantification-driven active
learning paradigm for learning dislocation mobility laws from automated
high-throughput large-scale molecular dynamics simulations, using Graph Neural
Networks (GNN) with a physics-informed architecture. We demonstrate that this
Physics-informed Graph Neural Network (PI-GNN) framework captures the
underlying physics more accurately compared to existing phenomenological
mobility laws in BCC metals.
