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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

AI-Driven Temperature Inference in XRD Patterns with Synthetic Pipelines and Molecular Dynamics Validation

Author(s)

Griffin Hess, Khai Minh Tran Nguyen, Ayoub Shahnazari, Zeliang Zhang, Chenliang Xu, Niaz Abdolrahim

On-Site Speaker (Planned)

Niaz Abdolrahim

Abstract Scope

Accurate lattice‐scale temperature measurement from X-ray diffraction (XRD) patterns is vital for probing phase transitions and defect dynamics at extreme conditions where conventional thermometry fails. Yet traditional methods are often qualitative and low-throughput. We extend our Auto Diffraction Pipeline (ADP) to generate temperature‐perturbed synthetic XRD data by embedding thermal diffuse scattering and phonon‐broadening effects into CIF‐based simulations. Convolutional neural networks trained on this enriched dataset learn to infer absolute temperature directly from diffraction snapshots. We validate AI predictions through non-equilibrium molecular dynamics simulations, correlating simulated thermal histories with diffraction-inferred temperatures. We further benchmarked against experimental XRD datasets for simple cubic materials such as copper. This integrated framework—combining synthetic pattern generation, deep learning inference, atomistic validation, and experimental benchmarking—offers rapid, quantitative thermometry and paves the way for data-driven materials characterization under extreme conditions.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, High-Temperature Materials, Characterization

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