Estimating yield-strain via deformation-recovery simulations

TL;DR

This paper introduces a novel deformation-recovery simulation method to estimate yield strain in crosslinked polymers, providing clearer signals and uncertainty quantification compared to traditional stress-strain analysis.

Contribution

It proposes a new approach using deformation-recovery data and hyperbolic regression to identify yield, improving accuracy and interpretability over existing methods.

Findings

Deformation-recovery data yields sharper yield signals.

Hyperbolic regression effectively models the transition to yield.

Method aligns well with recent experimental results.

Abstract

In computational materials science, predicting the yield strain of crosslinked polymers remains a challenging task. A common approach is to identify yield as the first critical point of stress-strain curves simulated by molecular dynamics (MD). However, in such cases the underlying data can be excessively noisy, making it difficult to extract meaningful results. In this work, we propose an alternate method for identifying yield on the basis of deformation-recovery simulations. Notably, the corresponding raw data (i.e. residual strains) produce a sharper signal for yield via a transition in their global behavior. We analyze this transition by non- linear regression of computational data to a hyperbolic model. As part of this analysis, we also propose uncertainty quantification techniques for assessing when and to what extent the simulated data is informative of yield. Moreover, we show how the method directly tests for yield via the onset of permanent deformation and discuss recent experimental results, which compare favorably with our predictions.