23 citations · 45 across the 6 of their papers we have counts for
Showing cond-mat.mtrl-sciShow all
2 papers · 1 filter
cond-mat.mtrl-sci2026★ 2 cited
High-quality, high-information datasets for universal atomistic machine learning
Cesare Malosso, Filippo Bigi, Paolo Pegolo +7
The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many wide…
cond-mat.mtrl-sci2025★ 10 cited
PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
Arslan Mazitov, Filippo Bigi, Matthias Kellner +6
Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of…