Machine learning approaches for intentional materials engineering

Yu‐chen Karen Chen‐Wiegart

doi:10.1557/s43577-025-00908-9

Machine learning approaches for intentional materials engineering

2025-05-14

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Yu‐chen Karen Chen‐Wiegart *

DOI：10.1557/s43577-025-00908-9

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The development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. This article explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. We highlight recent advancements in applying ML to nanostructured materials design via dealloying and discuss how techniques from other nanomaterial designs can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. Furthermore, we explore the role of ML in autonomous synchrotron x-ray experimentation, enabling real-time feedback between modeling and experimental setups. ML-driven approaches to microstructure characterization and mechanical property prediction are also examined, with a focus on modeling and advanced imaging techniques such as three-dimensional nanotomography. Finally, this article outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.

Keywords:

Artificial intelligence

Autonomous

Hierarchical

Machine learning

Morphology

Nanostructure

Porosity

X-ray tomography

Machine learning approaches for intentional materials engineering

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