E.v.a. Chaloupecká

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)...