Machine learning reveals strain-rate-dependent predictability of discrete dislocation plasticity
Marcin Mi\'nkowski, David Kurunczi-Papp, Lasse Laurson

TL;DR
This study uses machine learning and large-scale simulations to predict the stress-strain behavior of small crystals, revealing a complex, rate-dependent predictability influenced by dislocation dynamics and flow transition.
Contribution
It introduces a novel approach combining simulations and machine learning to analyze strain-rate effects on dislocation-mediated plasticity predictability.
Findings
Predictability improves with strain rate at small strains.
Predictability vs strain rate is non-monotonic at large strains.
Dislocation motion fraction explains rate-dependent predictability.
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…
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Taxonomy
TopicsMicrostructure and mechanical properties · Force Microscopy Techniques and Applications · Machine Learning in Materials Science
