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Meeting MS&T23: Materials Science & Technology
Symposium Leveraging Integrated Computational Materials Engineering for High-fidelity Physics-based and Machine Learning Models
Sponsorship TMS: ICME Committee
Organizer(s) William E. Frazier, Pacific Northwest National Laboratory
Philip E. Goins, Us Army Research Laboratory
Lei Li, Pacific Northwest National Laboratory
Yucheng Fu, Pacific Northwest National Laboratory
Scope This symposium will include topics pertaining to the improvement of microstructural models through linkages with experimental data, simulations at other length scales, and surrogate models. Applications related to microstructural evolution under processing and in-service are of particular interest. Topics may include:

- Integrated models of material deformation and microstructural evolution in metallic materials.
- 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 05/08/2023

Application of Machine Learning Framework in Predicting Creep Response of High Temperature Alloys
Hybrid Simulation Method Based on Molecular Dynamics and Machine Learning to Improve Property Prediction with Lower Computational Cost in Complex System
New Refractory High Entropy Alloys Discovery by Physics Discovery
Novel Convolutional-Recurrent Hybrid Neural Network for Predicting Fission Gas Release in UO2 Nuclear Fuel
Robotic Bending of Craniomaxillofacial Graft Fixation Plates
Simulating Macroscale Microstructures Using Advanced Programming and Numerical Methods

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