Machine Learning of First Principles Observables
E. Gelžinytė, Gábor Csányi, Janine George et al.
· 0 citations
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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