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Meeting MS&T26: Materials Science & Technology
Symposium Uncertainty Quantification in Ultra-High Temperature Materials Manufacturing
Presentation Title UHTM and the Materials R&D Landscape
Author(s) James A. Warren
On-Site Speaker (Planned) James A. Warren
Abstract Scope The application of materials design principles to ultra-high temperature materials is essential to realizing the technological promise of these materials, especially the challenges associated with insertion of a novel material into mission critical applications. Given the crucial role of microstructural variability, uncertainty quantification is a critical element of any R&D campaign to find the next great UHTM. In this context, the ideas, tools, and data developed within the Materials Genome Initiative (MGI), can provide the capability make next generation UHTM a reality. The MGI has laid the groundwork for the application of ICME, UQ, and now AI to this important class of materials, and will continue to be relevant to the materials design community in a time of a rapidly evolving R&D landscape.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Autonomous Materials Characterization Through Simulation to Experiment Analysis with Continual Deep Learning
Bayesian Design of Experiments for Calphad Modeling
Computational Tools for Predicting High-Temperature Materials Properties via DFT, MD, and Deep Learning
Data to Discovery: A Closed-Loop Ecosystem for Designing Compositionally Complex Alloys
End-to-End Machine Learning for Creep Modeling: Data Processing, Parameter Learning, and Uncertainty Analysis
From Design to Melt: Rare Earth Retention in Ni-Based Superalloys
From Dirty Processing to Enhanced Performance: Hidden Variables for Strength Consistency in UHTCs
Generalization of a Crystal Plasticity Model from Grade 91 to Grade 92 Steel: A Coupled High-Throughput Constitutive Model and Data-Driven Analysis Approach
MXene and Polymer Derived TiC–SiC Ceramics with Enhanced Electrical Conductivity and Tailored Thermal–Mechanical Performance for High-Temperature Applications
UHTM and the Materials R&D Landscape
Uncertainty-Guided Experimental Determination of Phase Diagrams
Uncertainty Quantification of In-Situ Densification of Polymer-Derived Ceramics
Uncertainty Quantification via Deep Kernel Learning on Synchrotron Diffraction Patterns

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