  Stress hotspots are regions of stress concentrations that form under
deformation in polycrystalline materials. We use a machine learning approach to
study the effect of preferred slip systems and microstructural features that
reflect local crystallography, geometry, and connectivity on stress hotspot
formation in hexagonal close packed materials under uniaxial tensile stress. We
consider two cases: one without any preferred slip systems with a critically
resolved shear stress (CRSS) ratio of 1:1:1, and a second with CRSS ratio
0.1:1:3 for basal: prismatic: pyramidal slip systems. Random forest based
machine learning models predict hotspot formation with an AUC (area under
curve) score of 0.82 for the Equal CRSS and 0.81 for the Unequal CRSS cases.
The results show how data driven techniques can be utilized to predict hotspots
as well as pinpoint the microstructural features causing stress hotspot
formation in polycrystalline microstructures
