Unsupervised learning for structure detection in plastically deformed crystals
arxiv(2022)
摘要
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.
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