Accurate and Efficient NMR Crystallography through Machine-Learning Geometry Optimization and Shielding Prediction

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.