Machine learning-informed structuro-elastoplasticity predicts ductility of disordered solids
Abstract
All solids yield under sufficiently high mechanical loads. Below yield, the mechanical responses of all disordered solids are nearly alike, but above yield every different disordered solid responds in its own way. Brittle systems can shatter without warning, like ordinary window glass, or exhibit strain localization prior to fracture, like metallic or polymeric glasses. Ductile systems, e.g. foams like shaving cream or emulsions like mayonnaise, can flow indefinitely with no strain localization. While there are empirical strategies for tuning the degree of strain localization, there is no framework that explains their effectiveness or limitations. We show that Structuro-Elastoplastic (StEP) models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity and elasticity.
Cite
@article{arxiv.2303.12486,
title = {Machine learning-informed structuro-elastoplasticity predicts ductility of disordered solids},
author = {Hongyi Xiao and Ge Zhang and Entao Yang and Robert J. S. Ivancic and Sean A. Ridout and Robert Riggleman and Douglas J. Durian and Andrea J. Liu},
journal= {arXiv preprint arXiv:2303.12486},
year = {2023}
}