Machine learning reveals strain-rate-dependent predictability of discrete dislocation plasticity

Abstract

Predicting the behaviour of complex systems is one of the main goals of science. An important example is plastic deformation of micron-scale crystals, a process mediated by collective dynamics of dislocations, manifested as broadly distributed strain bursts and significant sample-to-sample variations in the response to applied loading. Here, by combining large-scale discrete dislocation dynamics simulations and machine learning, we study the problem of predicting the fluctuating stress-strain curves of individual small single crystals subject to strain-controlled loading using features of the initial dislocation configurations as input. Our results reveal an intriguing rate dependence of deformation predictability: For small strains predictability improves with increasing strain rate, while for larger strains the predictability vs strain rate relation becomes non-monotonic. We show that for small strains the rate-dependence of deformation predictability can be captured by considering the fraction of dislocations moving against the direction imposed by the external stress, serving as a measure of strain-rate-dependent complexity of the dislocation dynamics. The non-monotonic predictability vs strain rate relation for large strains is argued to be related to a transition from fluctuating to smooth plastic flow when strain rate is increased.

References in corpus (17)

• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• Machine Learning Predicts Laboratory Earthquakes
• Strain rate dependency of dislocation plasticity
• Predicting plasticity in disordered solids from structural indicators
• Machine learning plastic deformation of crystals
• Coarse-graining of cellular automata, emergence, and the predictability of complex systems
• Predicting plastic flow events in athermal shear-strained amorphous solids
• Quenched pinning and collective dislocation dynamics
• Glassy features of crystal plasticity
• Plastic yielding and deformation bursts in the presence of disorder from coherent precipitates
• Machine learning and predicting the time dependent dynamics of local yielding in dry foams
• Machine learning depinning of dislocation pileups
• Dislocation avalanches from strain-controlled loading: A discrete dislocation dynamics study
• Probing the transition from dislocation jamming to pinning by machine learning
• Avalanche correlations and stress-strain curves in discrete dislocation plasticity
• Mimicking complex dislocation dynamics by interaction networks

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