A fine-tuned ViT on 8493 SEM images classifies fracture causes in zirconia-toughened alumina at 0.907 accuracy and 0.888 macro-F1, with comparable performance at 50x versus higher magnifications.
Machine learning of microstructure– property relationships in materials with robust features from foundational vision transformers
2 Pith papers cite this work. Polarity classification is still indexing.
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A survey of data-driven methods for materials modeling at nanoscale, mesoscale, and micro-to-continuum scales that identifies established capabilities, data quality issues, and obstacles to cross-scale integration.
citing papers explorer
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Low-Magnification SEM May Suffice: Interpretable Deep Learning for Multi-Scale Fracture-Cause Classification in Zirconia-Toughened Alumina
A fine-tuned ViT on 8493 SEM images classifies fracture causes in zirconia-toughened alumina at 0.907 accuracy and 0.888 macro-F1, with comparable performance at 50x versus higher magnifications.
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Materials Informatics Across the Length Scales
A survey of data-driven methods for materials modeling at nanoscale, mesoscale, and micro-to-continuum scales that identifies established capabilities, data quality issues, and obstacles to cross-scale integration.