How predictable is plastic damage at the atomic scale?

Abstract

The title of this letter implies two questions: To what degree is plastic damage inherently predictable at the atomic scale, and can this predictability be quantified? We answer these questions by combining image analysis with molecular dynamics (MD) simulation to quantify similarities between atomic structures of plastic damage in a database of strained copper bi-crystals. We show that a manifold of different outcomes can originate ostensibly from the same initial structure, but that with this approach complex plastic damage within this manifold can be statistically connected to the initial structure. Not only does this work introduce a powerful approach for analyzing MD simulations of a complex plastic damage but also provides a much needed and critical framework for analyzing and organizing atomic-scale microstructural databases.

Using spatial cross-correlation image analysis to characterize the influence of strain rate on plastic damage in molecular dynamics simulations

“…In two recent studies we showed that MD simulations of bi-crystal straining at a single strain rate of 10 9 s −1 can produce distinctly different damage configurations, even when started from ostensibly the same grain boundary structure [52,53]. This is largely due to different thermal fluctuations during the simulations.…”

“…This is largely due to different thermal fluctuations during the simulations. We also showed that image and statistical analysis can be used to quantify similarities in damage configurations among a data base of atomic configurations that were generated by MD simulation using different initial grain boundary tilt angles and inter-atomic potentials [52,53].…”

Statistical and image analysis for characterizing simulated atomic-scale damage in crystals

“…Motivated by the need for better tools for analyzing complex atomic defect structures, and encouraged by our previous results, [23] we report in the present paper results from a more comprehensive set of analyses using the set of atomic systems of strained bicrystals. This new study expands on the prior work in several important ways.…”

“…[17,18] In two recent studies, statistical and image analysis approaches were used to characterize plastic damage from MD simulations. [22,23] Both studies used coarse-grained matrices of central symmetry parameters (CSPs) [13] generated from a training set of plastic damage in strained bi-crystals containing two complementary symmetric grain boundaries with different tilt angles prepared as described in Section II below. The first study used three spatial regression approaches, a conditional autoregressive model, discrete wavelets, and principle component analysis, in an attempt to predict plastic damage at different tilt angles based on the training set.…”

“…In the second study, normalized spatial autocorrelation matrices were created from the CSP matrix of each MD simulation, and then cross-correlation (CC) matrices were generated from each of the auto-correlation matrices. [23] Using the largest element of the CC matrices as a predictor, the method was able to correctly connect the plastic damage in each simulation to within +/-5 o of the initial tilt angle for 87% of the simulations based on a data base of 30 MD simulations at each tilt angle.…”