73 citations · 181 across the 8 of their papers we have counts for
3 papers · 1 filter
Coherent energy and force uncertainty in deep learning force fields
Peter Bjørn Jørgensen, Jonas Busk, Ole Winther +1
In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quanti…
Danish Foundation Models
Kenneth Enevoldsen, Lasse Hansen, Dan S. Nielsen +10
Large language models, sometimes referred to as foundation models, have transformed multiple fields of research. However, smaller languages risk falling behind due to high training…
Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces
Jonas Busk, Mikkel N. Schmidt, Ole Winther +2
Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting an…