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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 Machine learning to derive effective transport properties of multiphase polycrystalline systems
Author(s) Nathan Bieberdorf, Kate Elder, Kwangnam Kim, Emily Moore, Stephen Weitzner, Tae Wook Heo
On-Site Speaker (Planned) Nathan Bieberdorf
Abstract Scope Effective engineering-scale properties of multi-phase polycrystalline systems are controlled by the topological arrangement of their underlying microstructures. Quantifying these structure-property relationships at the mesoscale and bridging up to engineering-scales remains challenging for highly heterogeneous microstructures. Here, we propose a data-driven, machine learning approach to upscaling, and apply it to effective diffusivity of multi-phase polycrystalline oxides relevant to nuclear reactors. First, we generate a diverse database of synthetic microstructures using active learning and compute their effective diffusivities using Fourier-spectral methods. We use random forest and neural network models to reveal the microstructure topology features that effective diffusivity is most sensitive to. From these down-selected features, we investigate using symbolic regression to derive physically verifiable structure-property relationships that can potentially extrapolate beyond the training domain and be upscaled to larger models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, Nuclear Materials

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