Foundation models for metallurgy?

D Marchand, MRS BULLETIN, 50, 805-818 (2025).

DOI: 10.1557/s43577-025-00911-0

Foundation models appear to promise precision atomic modeling across the periodic table, requiring little more than "fine-tuning" with a few density functional theory calculations. However, it is not clear whether they are sufficiently accurate, even with fine-tuning, for materials modeling to justify their high compute cost. Here, we compare state-of- the art foundation models against a collection of "bespoke" neural network potentials for the Al-Cu-Mg-Zn system. While we find many foundation models to give poor or very poor results, some such as GRACE2L-OAM offer extremely good accuracy in most cases. We find that models trained on the defect-containing and higher k-point density "Alexandria" data set had much better performance than those trained on Materials Project data alone. Our results also indicate that thermal conductivity scores are a much better indicator of metallurgical performance than energy errors on the convex hull.

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