Paper

Building Data-driven Models with Microstructural Images: Generalization and Interpretability

As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structure-property relations throughout materials science, it seems logical that models that can leverage microstructural data would be more capable of predicting property information... (read more)

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