8 papers
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…
A universal machine learning model for the electronic density of states
Wei Bin How, Pol Febrer, Sanggyu Chong +5
In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbit…
Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
Filippo Bigi, Joseph W. Abbott, Philip Loche +12
Incorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce the computational c…