3 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…
cond-mat.stat-mech2026
Thermodynamic properties of chemically disordered compounds via AI-driven estimation of partition function with the PULSE method
Baptiste Bernard, Luca Messina, Eiji Kawasaki +1
In this article, we present an improved version of the PULSE method (Partition function Unsupervised Learning Sampling and Evaluation) for estimating the thermodynamic properties o…