2 papers
physics.chem-ph2026
Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
Riccardo Farris, Emanuele Telari, Nongnuch Artrith +2
Neural-network-based machine learning interatomic potentials have emerged as powerful tools for predicting atomic energies and forces, enabling accurate and efficient simulations i…
cond-mat.mtrl-sci2026
Structural Chart of Copper-Silver Nanoalloys through machine learning
Manoj Settem, Emanuele Telari, Antonio Tinti +2
Nanoalloys (or alloy nanoparticles) are an important class of materials that are promising for their functional properties. However, designing synthesis protocols to control their…