Author

Matthias Kellner

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Preprint Oct 2026

Unconstrained learning of equivariant atomistic properties

Machine-learned interatomic potentials based on graph neural networks (GNNs) have made first-principles accuracy routine for the potential energy surface (PES). Their backbones, often O(3)-equivariant by design, are increasingly used to learn other atomistic properties. Many of these are of high angular order or pseudo...

Joseph W. Abbott, Paolo Pegolo, Pol Febrer et al. · 0 citations

Preprint Aug 2026

Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections

Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictions have matured significantly and today are primarily limited by the electronic structure reference data they are trained on. Here, we intro...

Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes et al. · 0 citations