3 papers
cond-mat.mtrl-sci2026
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…
physics.chem-ph2026
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…
physics.chem-ph2022
Understanding chemical reactions via variational autoencoder and atomic representations
Martin Šípka, Andreas Erlebach, Lukáš Grajciar
On the time scales accessible to atomistic numerical modelling, chemical reactions are considered rare events. Atomistic simulations are typically biased along a low-dimensional re…