most citedPushing the limits of unconstrained machine-learned interatomic potentials

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cond-mat.mtrl-sci2026

AI-Driven Expansion and Application of the Alexandria Database

Théo Cavignac, Jonathan Schmidt, Pierre-Paul De Breuck +9

We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stabili…

cond-mat.mtrl-sci2025

Machine-Learning-enabled ab initio study of quantum phase transitions in SrTiO

Jonathan Schmidt, Nicola A. Spaldin

We use the self-consistent harmonic approximation (SSCHA) with machine learning interatomic potentials to calculate the effect of O substitution on the properties of quantum…