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Meeting MS&T25: Materials Science & Technology
Symposium Integrated Computational Materials Engineering for Physics-Based Machine Learning Models
Organizer(s) William E. Frazier, Pacific Northwest National Laboratory
Zhengtao Gan, Arizona State University
Lei Li, Pacific Northwest National Laboratory
Yucheng Fu, Pacific Northwest National Laboratory
Philip Goins, Army Research Laboratory
Scope This symposium will include topics pertaining to the improvement and deployment of computational materials approaches through linkages with experimental data, simulations at other length scales, and surrogate models. Applications related to microstructural evolution, mechanical behavior, degradation during processing and in-service are of particular interest. Topics may include:

- Integrated models of material deformation and microstructural evolution in metallic and non-metallic materials.
- Integrated models of microstructural evolution and material degradation.
- Integrated models of material process-structure-property relationship.
- Approaches for the simulation of grain growth, recrystallization, twinning, phase transformations, or related phenomena coupling multiple scale modeling approaches.
- Verification/validation of machine learning models [e.g., physics-informed or data-driven methods] of microstructural evolution and/or response to deformation, aging, and irradiation.
- Novel couplings of experimental methods and data with mesoscale modeling approaches.
- Models using physics-informed or generative machine learning approaches to model the formation of microstructures or microstructural evolution processes.

Abstracts Due 05/01/2025
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