Machine learning depinning of dislocation pileups

Abstract

We study a one-dimensional model of a dislocation pileup driven by an external stress and interacting with random quenched disorder, focusing on predictability of the plastic deformation process. Upon quasistatically ramping up the externally applied stress from zero the system responds by exhibiting an irregular stress--strain curve consisting of a sequence of strain bursts, i.e., critical-like dislocation avalanches. The strain bursts are power-law distributed up to a cutoff scale which increases with the stress level up to a critical flow stress value. There, the system undergoes a depinning phase transition and the dislocations start moving indefinitely, i.e., the strain burst size diverges. Using sample-specific information about the pinning landscape as well as the initial dislocation configuration as input, we employ predictive models such as linear regression, simple neural networks and convolutional neural networks to study the predictability of the simulated stress--strain curves of individual samples. Our results show that the response of the system -- including the flow stress value -- can be predicted quite well, with the correlation coefficient between predicted and actual stress exhibiting a non-monotonic dependence on strain. We also discuss our attempts to predict the individual strain bursts.

arXiv comment10 pages, 10 figures

References in corpus (24)

• Adam: A Method for Stochastic Optimization
• Scikit-learn: Machine Learning in Python
• Machine learning and the physical sciences
• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• Learning data driven discretizations for partial differential equations
• Machine Learning Predicts Laboratory Earthquakes
• Dynamics of a ferromagnetic domain wall: avalanches, depinning transition and the Barkhausen effect
• Discovering Symbolic Models from Deep Learning with Inductive Biases
• Crackling dynamics in material failure as the signature of a self-organized dynamic phase transition
• Avalanches and clusters in planar crack front propagation
• Machine learning plastic deformation of crystals
• Depinning transition of dislocation assemblies: pileup and low-angle grain boundary
• Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
• Quenched pinning and collective dislocation dynamics
• Glassy features of crystal plasticity
• Dynamical correlations near dislocation jamming
• Excitation spectra in 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
• Prediction in a driven-dissipative system displaying a continuous phase transition
• Learning Composable Energy Surrogates for PDE Order Reduction
• Slip line growth as a critical phenomenon
• Mimicking complex dislocation dynamics by interaction networks

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