146 citations · 199 across the 6 of their papers we have counts for
6 papers
Synthetic pre-training of graph-network models for predicting solid-state NMR parameters
Chiheb Ben Mahmoud, Carlos Bornes, Christopher J. Heard +3
Nuclear magnetic resonance (NMR) is a powerful probe of atomic structure, but accurate quantum-mechanical predictions of tensorial NMR parameters are computationally demanding. Thi…
An Accurate Tensorial Model for Prediction of Full Zeolite NMR Spectra
Carlos Bornes, Chiheb Ben Mahmoud, Volker L. Deringer +2
Solid state nuclear magnetic resonance (ss-NMR) is one of the most sensitive and popular techniques for structure elucidation in geometrically complex crystalline materials, such a…
Distillation of atomistic foundation models across architectures and chemical domains
John L. A. Gardner, Daniel F. Thomas du Toit, Chiheb Ben Mahmoud +8
Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trai…
Robustness of Local Predictions in Atomistic Machine Learning Models
Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud +3
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is co…
Predicting hot-electron free energies from ground-state data
Chiheb Ben Mahmoud, Federico Grasselli, Michele Ceriotti
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation te…
Learning the electronic density of states in condensed matter
Chiheb Ben Mahmoud, Andrea Anelli, Gábor Csányi +1
The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture, and is central to modern elect…