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
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
Sauradeep Majumdar, Miguel Steiner, Johannes C. B. Dietschreit +4
Free energy profiles serve as a fundamental bridge between microscopic atomic fluctuations and macroscopic thermodynamic observables. Estimating the free energy profile along a rea…
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…