3 papers
physics.chem-ph2026
Pushing the limits of unconstrained machine-learned interatomic potentials
Filippo Bigi, Paolo Pegolo, Arslan Mazitov +2
Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The…
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…