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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Causally Informed Machine Learning for Nanoporous Microstructure-Property Relationships
Author(s) Longsheng Feng, Tae Wook Heo, Juergen Biener
On-Site Speaker (Planned) Longsheng Feng
Abstract Scope Nanoporous materials exhibit complex microstructure-property relationships that are central to their performance in applications involving transport, mechanics, catalysis, energy storage, separation, and radiation tolerance. However, the complexity of these relationships makes it difficult to identify the physical origins of property variations and to design microstructures for targeted applications. In this work, we use simulated bicontinuous porous microstructures to generate a numerical dataset for data-driven analysis of microstructure-aware properties. Interpretable machine learning is first applied to identify key microstructural features governing transport and mechanical responses. Causal analysis is then used to distinguish descriptors that are predictive from those that serve as actionable microstructural levers. The causal framework further reveals distinct microstructural regimes based on intervention behavior, providing guidance for regime-aware microstructure design. Together, these results demonstrate how combining machine learning with causal reasoning can move microstructure-property analysis beyond correlation-based prediction toward interpretable and intervention-aware design of nanoporous materials.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Composites

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