Phase‐Field Crystal Modeling: Integrating Density Functional Theory, Molecular Dynamics, and Phase‐Field Modeling

Zaeem, Mohsen Asle; Asadi, Ebrahim

doi:10.1002/9781119018377.ch3

Cited by 2 publications

(1 citation statement)

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“…However, such models are limited due to uncertainties in atomistic estimation of parameters used in the computation of rate constants, commonly obtained from activation energies derived from transition state theory [14]. Phase Field Crystal (PFC) is another fast growing method for simulation of crystalline structures with atomistic detail, while reaching diffusive time-scales, and has been used to model dislocation dynamics [17,4,50,1].…”

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confidence: 99%

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

Moraes¹,

Suzuki²,

Zayernouri³

2020

Preprint

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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.

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confidence: 99%