Predicting glass-forming ability of metallic alloys

Predict from composition alone whether an alloy forms a bulk metallic glass and how thick it can be cast, and use this to find new glass formers.

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@misc{cairn-metallic-glass-forming-ability,
  title        = {Predicting glass-forming ability of metallic alloys},
  author       = {{Cairn Commons contributors}},
  howpublished = {\url{https://cairn-commons.com/problems/metallic-glass-forming-ability}},
  year         = {2026},
  note         = {Open problem on Cairn Commons, CC BY 4.0. Accessed 2026-09-28}
}

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The problem

Metallic glasses form when a melt is cooled fast enough to avoid crystallisation. The open question: can glass-forming ability — e.g. the critical casting diameter or critical cooling rate — be predicted from composition alone, without first measuring characteristic temperatures of the alloy?

Known status. The first reported metallic glass, Au75Si25, was made by Klement, Willens and Duwez in 1960 with cooling rates of order 10^6 K/s; by 1990 some multicomponent alloys vitrified at around 1 K/s. Empirical rules (three or more components, significant atomic size mismatch, negative mixing enthalpy) are known but not predictive. Machine learning helps but has caveats: Ren, Ward, Wolverton, Hattrick-Simpers, Mehta et al. (Science Advances 2018) iterated ML and high-throughput experiments and found three new glass-forming systems; a random-forest model on 715 compositions reached test R^2 ≈ 0.95 for maximum diameter (Scientific Reports 2022), but it uses measured Tg, Tx and Tl as inputs — the prediction from composition alone remains the harder, open task.

What counts as progress

Composition-only models on public datasets with code and splits released, evaluated on held-out alloy systems (not just held-out compositions within a system).
Physics-based or simulation-based descriptors (e.g. from molecular dynamics or thermodynamic databases) whose predictive value is tested reproducibly.
Curated, de-duplicated public datasets of critical casting diameters with provenance.
Documented negative results: a feature set that fails when entire systems are held out.

How it is checked. A reviewer re-runs training and evaluation and checks that splits are by alloy system, that inputs do not include quantities only available after synthesis (unless stated), and that reported metrics match.

Current state

The problem