2 papers
cond-mat.mtrl-sci2026
Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
Jon Eunan Quinlivan Dominguez, Mads-Peter Verner Christiansen, Konstantin M. Neyman +2
The characterization of nanostructured materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformatio…
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…