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Abstract <p>&#xa0;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.</p> Graphic abstract <p></p>

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Constructing generalizable microstructure–property maps across diverse microstructure classes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月6日

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  • Hao Liu,
  • Nirmal Baishnab,
  • Balaji S. Sarath Pokuri,
  • Baskar Ganapathysubramanian,
  • Olga Wodo

摘要

Abstract

 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.

Graphic abstract