Author

Silvia Bonfanti

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Open access Sep 2026

Data-driven prediction of thin-film metallic glass forming ability via Bayesian classification and experimental verification in the Cu–Zr–Al system

Identifying alloy compositions suitable for metallic glass formation from the vast and multi-dimensional space of elemental combinations remains a significant challenge. In the present study, we develop—using literature data—a Bayesian machine learning model based on Gaussian process classification for predicting the g...

Xu-Liang Luo, T. Mäkinen, Wen-Yi Huo et al. · RSC Advances · 0 citations

Preprint Jul 2026

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles

Dyna-Mat-v1.0 shows that end-to-end finite-temperature validation is essential for quantifying the predictive behaviour of foundation MLIPs, and provides a simple, scalable route for assessing them beyond static and harmonic benchmarks relevant to materials design.

Mikołaj J Gawkowski, Nongnuch Artrith, Silvia Bonfanti et al. · 1 citation