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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

Data-driven and LLM-based design of hydrogen solubilities in metallic alloys

Author(s)

Tilmann Hickel, Shankha Nag, Ali Tehranchi, Alexander Kister

On-Site Speaker (Planned)

Tilmann Hickel

Abstract Scope

One key goal in hydrogen economy is to control the amount of hydrogen that can be incorporated into a metallic crystal structure. First, a large dataset for the solution enthalpy of interstitials in transition metals has been generated by density functional theory (DFT) to identify universal trends and descriptors. Second, we systematically analyze with DFT the impact of Cr, Mn, Fe on the H solubility in compounds. We identify magneto-volume effects as the origin for strongly nonlinear trends. Based on these insights, the reasoning abilities of large-language model (LLMs) are validated. It is asked to use the provided database, as well as explicate and implicit knowledge about the underlying mechanisms to predict the H solution enthalpy for composition ranges that were not part of the training set. While the reasoning of the LLM in terms of programming code leads to transparent models, advantages of exploiting FAIR workflow solutions are emphasized.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning,

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