  Collective motion of dislocations is governed by the obstacles they
encounter. In pure crystals, dislocations form complex structures as they
become jammed by their anisotropic shear stress fields. On the other hand,
introducing disorder to the crystal causes dislocations to pin to these
impeding elements and, thus, leads to a competition between
dislocation-dislocation and dislocation-disorder interactions. Previous studies
have shown that, depending on the dominating interaction, the mechanical
response and the way the crystal yields change.
  Here we employ three-dimensional discrete dislocation dynamics simulations
with varying density of fully coherent precipitates to study this phase
transition $-$ from jamming to pinning $-$ using unsupervised machine learning.
By constructing descriptors characterizing the evolving dislocation
configurations during constant loading, a confusion algorithm is shown to be
able to distinguish the systems into two separate phases. These phases agree
well with the observed changes in the relaxation rate during the loading. Our
results also give insights on the structure of the dislocation networks in the
two phases.

Materials Science;Statistical Mechanics;Computational Physics