6 citations · 7 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Kapitza stabilization of quantum critical order
Dushko Kuzmanovski, Jonathan Schmidt, Nicola A. Spaldin +3
Dynamical perturbations modify the states of classical systems in surprising ways and give rise to important applications in science and technology. For example, Floquet engineerin…