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

TL;DR

This paper introduces a graph-based surrogate model for simulating dislocation glide in materials, enabling fast, accurate, and uncertainty-aware multi-scale modeling from atomistic to continuum levels.

Contribution

It presents a novel graph surrogate model that captures dislocation mobility using a stochastic process, bridging atomistic simulations and continuum mechanics efficiently.

Findings

Achieves significant computational speed-up over molecular dynamics.

Recovers atomistic mobility estimates with high accuracy.

Provides statistical insights into dislocation mobility and uncertainty propagation.

Abstract

From their birth in the manufacturing process, materials inherently contain defects that affect the mechanical behavior across multiple length and time-scales, including vacancies, dislocations, voids and cracks. Understanding, modeling, and real-time simulation of the underlying stochastic micro-structure defect evolution is therefore vital towards multi-scale coupling and propagating numerous sources of uncertainty from atomistic to eventually aging continuum mechanics. We develop a graph-based surrogate model of dislocation glide for computation of dislocation mobility. We model an edge dislocation as a random walker, jumping between neighboring nodes of a graph following a Poisson stochastic process. The network representation functions as a coarse-graining of a molecular dynamics simulation that provides dislocation trajectories for an empirical computation of jump rates. With this construction, we recover the original atomistic mobility estimates, with remarkable computational speed-up and accuracy. Furthermore, the underlying stochastic process provides the statistics of dislocation mobility associated to the original molecular dynamics simulation, allowing an efficient propagation of material parameters and uncertainties across the scales.