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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Posing Inverse Design of Mg-Alloy Microstructure and Texture as a Physics Guided Latent Diffusion-Optimization Problem

Author(s)

Mahish Kumar Guru, Jan Bohlen, Roland Aydin , Noomane Ben Khalifa

On-Site Speaker (Planned)

Mahish Kumar Guru

Abstract Scope

Tailoring high-performance magnesium alloys requires effective inverse design optimization of microstructure and texture. This work introduces a practical framework integrating experimental data, physics-based simulation, and generative AI. Grounded with an experimental dataset of 115 extruded Mg alloys, we use data multiplication techniques to generate a large corpus of synthetic RVEs, which are then labeled with properties using an experimentally validated Crystal Plasticity Finite Element Method model. Generative models (Autoregressive/Diffusion) learn a compressed latent representation of these RVEs. A Gaussian Process surrogate maps this latent space to properties, guiding a Bayesian Optimization loop. In each iteration, the optimizer proposes promising latent points, the generative model reconstructs the RVE, and CPFEM provides physics-based validation in an active-learning cycle. By seamlessly combining the speed of generative AI, the rigor of physics-based validation, and the grounding of experimental data, this hybrid framework offers a practical path for accelerated materials inverse design.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Magnesium,

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