37 research outputs found
A nonplanar Peierls-Nabarro model and its applications to dislocation cross-slip
A novel semidiscrete Peierls-Nabarro model is introduced which can be used to
study dislocation spreading at more than one slip planes, such as dislocation
cross-slip and junctions. The strength of the model, when combined with ab
initio calculations for the energetics, is that it produces essentiallyan
atomistic simulation for dislocation core properties without suffering from the
uncertainties associated with empirical potentials. Therefore, this method is
particularly useful in providing insight into alloy design when empirical
potentials are not available or not reliable for such multi-element systems. As
an example, we study dislocation cross-slip and constriction process in two
contrasting fcc metals, Al and Ag. We find that the screw dislocation in Al can
cross-slip spontaneously in contrast with that in Ag, where the screw
dislocation splits into two partials, which cannot cross-slip without first
being constricted. The response of the dislocation to an external stress is
examined in detail. The dislocation constriction energy and the critical stress
for cross-slip are determined, and from the latter, we estimate the cross-slip
energy barrier for straight screw dislocations.Comment: Submitted for the Proceedings of Multiscale Modelling of Materials
(London, 2002
Computationally-efficient stochastic cluster dynamics method for modeling damage accumulation in irradiated materials
An improved version of a recently developed stochastic cluster dynamics (SCD)
method {[}Marian, J. and Bulatov, V. V., {\it J. Nucl. Mater.} \textbf{415}
(2014) 84-95{]} is introduced as an alternative to rate theory (RT) methods for
solving coupled ordinary differential equation (ODE) systems for irradiation
damage simulations. SCD circumvents by design the curse of dimensionality of
the variable space that renders traditional ODE-based RT approaches inefficient
when handling complex defect population comprised of multiple (more than two)
defect species. Several improvements introduced here enable efficient and
accurate simulations of irradiated materials up to realistic (high) damage
doses characteristic of next-generation nuclear systems. The first improvement
is a procedure for efficiently updating the defect reaction-network and event
selection in the context of a dynamically expanding reaction-network. Next is a
novel implementation of the -leaping method that speeds up SCD
simulations by advancing the state of the reaction network in large time
increments when appropriate. Lastly, a volume rescaling procedure is introduced
to control the computational complexity of the expanding reaction-network
through occasional reductions of the defect population while maintaining
accurate statistics. The enhanced SCD method is then applied to model defect
cluster accumulation in iron thin films subjected to triple ion-beam
(, and \text{H\ensuremath{{}^{+}}})
irradiations, for which standard RT or spatially-resolved kinetic Monte Carlo
simulations are prohibitively expensive
Learning dislocation dynamics mobility laws from large-scale MD simulations
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
Dislocation constriction and cross-slip in Al and Ag: an ab initio study
A novel model based on the Peierls framework of dislocations is developed.
The new theory can deal with a dislocation spreading at more than one slip
planes. As an example, we study dislocation cross-slip and constriction process
of two fcc metals, Al and Ag. The energetic parameters entering the model are
determined from ab initio calculations. We find that the screw dislocation in
Al can cross-slip spontaneously in contrast with that in Ag, which splits into
partials and cannot cross-slip without first being constricted. The dislocation
response to an external stress is examined in detail. We determine dislocation
constriction energy and critical stress for cross-slip, and from the latter, we
estimate the cross-slip energy barrier for the straight screw dislocations