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From their birth in the manufacturing process, materials inherently containdefects that affect the mechanical behavior across multiple length andtime-scales, including vacancies, dislocations, voids and cracks.Understanding, modeling, and real-time simulation of the underlying stochasticmicro-structure defect evolution is therefore vital towards multi-scalecoupling and propagating numerous sources of uncertainty from atomistic toeventually aging continuum mechanics. We develop a graph-based surrogate modelof dislocation glide for computation of dislocation mobility. We model an edgedislocation as a random walker, jumping between neighboring nodes of a graphfollowing a Poisson stochastic process. The network representation functions asa coarse-graining of a molecular dynamics simulation that provides dislocationtrajectories for an empirical computation of jump rates. With thisconstruction, we recover the original atomistic mobility estimates, withremarkable computational speed-up and accuracy. Furthermore, the underlyingstochastic process provides the statistics of dislocation mobility associatedto the original molecular dynamics simulation, allowing an efficientpropagation of material parameters and uncertainties across the scales.

Atomistic-to-Meso Multi-Scale Data-Driven Graph Surrogate Modeling of Dislocation Glide