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. While there have […]