Unsupervised learning for structure detection in plastically deformed crystals

TL;DR

This paper presents an unsupervised learning algorithm for automatic, precise detection of structural features in plastically deformed crystals, improving upon traditional criteria-based methods.

Contribution

It introduces a fast, easy-to-implement unsupervised algorithm that enhances structure detection accuracy in crystalline materials under deformation.

Findings

Detects more structures than traditional methods

Achieves higher detection precision

Computationally efficient and easy to implement

Abstract

Detecting structures at the particle scale within plastically deformed crystalline materials allows a better understanding of the occurring phenomena. While previous approaches mostly relied on applying hand-chosen criteria on different local parameters, these approaches could only detect already known structures.We introduce an unsupervised learning algorithm to automatically detect structures within a crystal under plastic deformation. This approach is based on a study developed for structural detection on colloidal materials. This algorithm has the advantage of being computationally fast and easy to implement. We show that by using local parameters based on bond-angle distributions, we are able to detect more structures and with a higher degree of precision than traditional hand-made criteria.