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Constructing generalizable microstructure-property maps across diverse microstructure classes
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AI
DOI：10.1557/s43579-025-00873-z.png)
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Abstract
En 中文
Establishing microstructure-property correlations that generalize across diverse microstructural classes remains a critical challenge in data-driven materials design. In this work, we evaluate the potential to extrapolate predictive models trained on one microstructure type (e.g., spinodal) to others (e.g., dendritic), using three distinct featurization strategies: two-point correlation functions, graph-based descriptors, and deep neural network embeddings. Our findings reveal that the Wasserstein distance is an excellent metric that correlates well with generalizability, serving as a model-agnostic yet data-aware signature of generalizability. Furthermore, we demonstrate that featurizations that conserve key microstructural features generalize better.
Keywords:
Microstructure
Machine Learning
Journal
IF:
2.3
Papers:
222
Citations:
2.8K