Accessible hybrid DFT-quality NMR crystallography via gas-phase machine learning interatomic potentials

Nuclear magnetic resonance (NMR) crystallography is a robust method for structure determination, but its reliance on density functional theory (DFT) for geometry refinement limits its speed and accessibility. Recent machine‑learning predictors such as ShiftML3 can evaluate magnetic shieldings in seconds, but they still need high‑quality crystal geometries that are normally obtained from slow DFT optimisations. Here we demonstrate that machine learning interatomic potentials (MLIPs) can eliminate this bottleneck for organic crystals. Interestingly, models trained on gas-phase molecules at the hybrid ωB97M-V level (OMol25 dataset) — such as UMA- omol and MACE-Polar-1 — deliver structural quality rivaling hybrid periodic-DFT, without requiring large computational resources. The resulting MLIP–ShiftML3 workflow reduces computational cost by at least three orders of magnitude, making high-accuracy NMR crystallography accessible without HPC infrastructure, and opening the door to fast molecular dynamics simulations at the hybrid DFT level. Combined with sensitivity enhancement via dynamic nuclear polarization (DNP), we demonstrate the approach on L-histidine, extracting ¹³C and ¹⁵N chemical shift tensors and using ¹H chemical shifts at natural isotopic abundance and discriminating between its monoclinic and orthorhombic polymorphs.