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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

Bayesian Inference of Local, Temperature-Dependent, Anisotropic Thermal Conductivity from Modulated Photothermal Phase Data

Author(s)

Kyle Joshua Kelley, Troy R Munro, Oliver K Johnson

On-Site Speaker (Planned)

Kyle Joshua Kelley

Abstract Scope

Reliable thermal conductivity characterization with rigorous uncertainty quantification (UQ) is essential for predictive thermal transport simulations in complex material systems, which in turn support robust materials design and analysis under real-world variability. To meet this need, we present a framework for inferring local, temperature-dependent, anisotropic thermal conductivity from raw photothermal phase data, incorporating robust UQ via Bayesian statistical methods. The effectiveness of this approach is then demonstrated through the characterization of the temperature-dependent principal thermal conductivities of a representative, anisotropic, single-crystal sample.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Characterization

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