Konstantin M. Neyman

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials

Abstract Neural-network-based machine learning interatomic potentials have emerged as powerful tools for predicting atomic energies and forces, enabling accurate and efficient simulations in atomistic modeling. A key limitation of traditional deep learning approaches, however, is...