We evaluated a fully machine-learning-assisted workflow for NMR crystallography by combining the Universal Model for Atoms (UMA) interatomic potential for crystal structure optimization with the ShiftML3 prediction of solid-state NMR shieldings. Benchmarking against conventional periodic density functional theory (DFT) calculations for 1H, 13C, and 15N chemical shifts demonstrates that ML-based geometry optimization consistently improves the accuracy of 13C and 15N predictions relative to standard PBE optimization, highlighting the dominant role of structural refinement. ShiftML3 achieves DFT-level accuracy for shielding prediction and, when combined with UMA-optimized geometries, matches or surpasses periodic DFT for 13C and 15N while reducing the computational cost by orders of magnitude. We further show that hybrid PBE0 single-molecule corrections remain effective for both DFT- and ShiftML3-derived shieldings, extending their applicability to modern machine-learning models. These results establish a new computational paradigm for NMR crystallography by replacing both computational bottlenecks of the conventional DFT workflow with modern machine-learning models.
E.v.a. Chaloupecká, O. Socha, Martin Dračínský· Journal of Physical Chemistr...· 0 citations
Accurately predicting NMR chemical shifts of exchangeable protons in solution remains challenging because of the combined influence of solute–solvent interactions and molecular dynamics. We introduce a framework that integrates machine-learning molecular dynamics (ML-MD) with the ShiftML3 machine-learning shielding model for rapid and accurate prediction of NMR spectra in solvated molecules. Although originally developed for solids, ShiftML3 effectively captures intermolecular contributions to shielding in solution. We validate the method across a range of chemically diverse systems, including water in organic solvents, solvated alcohols, hydrogen-bonded nucleobases, glucose anomers, and alkylated acetamides. The ML-MD + ShiftML3 framework reproduces experimentally observed chemical shifts of exchangeable protons with near-quantitative accuracy, resolving subtle hydrogen-bonding and conformational effects that implicit-solvent DFT fails to capture. These results establish ML-MD + ShiftML3 as a transferable and computationally efficient way of incorporating solvation and dynamics into NMR spectroscopy, enabling realistic chemical shift predictions for flexible, hydrogen-bonded, and complex molecular systems. The authors develop a framework integrating machine-learning molecular dynamics with the ShiftML3 machine-learning shielding mode to accurately predict NMR chemical shifts of exchangeable protons in solution, outperforming DFT approaches in capturing solvation effects.
O. Socha, Jana Pavlišová, Debashree Manna et al.· Nature Communications· 2 citations
