66 citations · 109 across the 11 of their papers we have counts for
5 papers · 1 filter
Unconstrained learning of equivariant atomistic properties
Joseph W. Abbott, Paolo Pegolo, Pol Febrer +6
Machine-learned interatomic potentials based on graph neural networks (GNNs) have made first-principles accuracy routine for the potential energy surface (PES). Their backbones, of…
Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections
Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes +4
Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictio…
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
Matthias Kellner, Teitur Hansen, Thomas Bligaard +2
Machine-learning models of atomic-scale interactions achieve the accuracy of the quantum mechanical calculations on which they are trained, but at a dramatically lower computationa…
Quantum-corrected NMR crystallography at scale
Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes +4
Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, comp…
How to Train a Shallow Ensemble
Moritz Schäfer, Matthias Kellner, Johannes Kästner +1
Shallow ensembles provide a convenient strategy for uncertainty quantification in machine learning interatomic potentials, that is computationally efficient because the different e…