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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Verification, Calibration, and Validation Approaches in Modeling the Mechanical Performance of Metallic Materials
Presentation Title Latent Learning for Predictive and Generative Modeling of Microstructure-Property Relationships in Metals
Author(s) Marc J. Murphy
On-Site Speaker (Planned) Marc J. Murphy
Abstract Scope This work presents a modular, data-driven workflow that deconstructs complex 3D microstructures and independently encodes their constituent features as structured latent representations, hierarchically fused for downstream predictive and generative tasks. Demonstrated on the Ni-based superalloy LSHR, individual grain shapes from synthetic RVEs and HEDM reconstructions are embedded as continuous latents via supervised, spectral-normalized CNN autoencoders, with generative sampling enabled through score-based latent diffusion modeling. Scalability is achieved by tokenizing grain-shape latents and conditioning them on crystallographic orientation and centroid position for microstructure-level reconstruction and property prediction using transformer networks. Trained across a diverse range of microstructures, the framework captures detailed geometric features independent of material class while flexibly integrating material-specific attributes via attention-based conditioning. By scaling grain-level encodings to full microstructures, the model can predict heterogeneous elastic strain with <1% RMSE. This generalizable latent representation enables extensions to high-precision fatigue indicator parameter (FIP) mapping, plasticity modeling, and inverse microstructure design.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Mechanical Properties, Modeling and Simulation

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