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Stress hotspots are regions of stress concentrations that form underdeformation in polycrystalline materials. We use a machine learning approach tostudy the effect of preferred slip systems and microstructural features thatreflect local crystallography, geometry, and connectivity on stress hotspotformation in hexagonal close packed materials under uniaxial tensile stress. Weconsider two cases: one without any preferred slip systems with a criticallyresolved shear stress (CRSS) ratio of 1:1:1, and a second with CRSS ratio0.1:1:3 for basal: prismatic: pyramidal slip systems. Random forest basedmachine learning models predict hotspot formation with an AUC (area undercurve) 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 hotspotsas well as pinpoint the microstructural features causing stress hotspotformation in polycrystalline microstructures

Applied Machine Learning to Predict Stress Hotspots II: Hexagonal close packed materials