2 papers
physics.chem-ph2026
Contrastive Regularization of Machine Learning Potentials
Dimitrios Tzivrailis, Georgios Sotiropoulos, Alberto Rosso +1
Machine learning interatomic potentials are trained to predict energies and forces but built to be sampled: their purpose is to drive molecular simulations whose observables averag…
cond-mat.dis-nn2026
Uncertainty in AI-driven Monte Carlo simulations
Dimitrios Tzivrailis, Alberto Rosso, Eiji Kawasaki
In the study of complex systems, evaluating physical observables often requires sampling representative configurations via Monte Carlo techniques. These methods rely on repeated ev…