Unsupervised learning for structure detection in plastically deformed crystals
This work addresses the challenge of identifying unknown structures in crystalline materials for materials science researchers, though it is incremental as it adapts an existing method from colloidal materials.
The authors tackled the problem of detecting particle-scale structures in plastically deformed crystals by introducing an unsupervised learning algorithm, which detected more structures with higher precision than traditional hand-chosen criteria.
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.