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…