Quantum-corrected NMR crystallography at scale

TL;DR

This paper introduces a quantum-nuclei-corrected approach for NMR crystallography that improves the accuracy of chemical-shielding predictions by using machine learning generated quantum ensembles, enabling better structure determination especially for hydrogen-bonded protons.

Contribution

The authors develop a novel machine learning model, PET-MOLS, to generate quantum ensembles that enhance NMR shielding predictions without high computational costs.

Findings

Two-fold improvement in hydrogen-bonded proton shielding predictions.

Enhanced agreement with experimental NMR data.

Scalable method applicable to amorphous materials.

Abstract

Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, computational cost limits the accuracy of shielding predictions, that usually rely on low-level electronic-structure approximations and neglect thermal and quantum mechanical nuclear motion, leading to large errors, especially for highly informative hydrogen-bonded protons. To address these limitations, we introduce a quantum-nuclei-corrected (QNC-NMR) approach. We generate inexpensively quantum ensembles using PET-MOLS, a novel machine-learning learning model of the interatomic potential transferable across molecular crystals. Using them as inputs to a chemical-shift model results in a two-fold improvement of the agreement with experiments for hydrogen-bonded protons, without the need for empirical corrections. The ability to sample structures consistent with the experimental conditions enables further refinement of the shielding model by finetuning it against measured shieldings. The favorable scaling with system size allows similar improvements for amorphous materials that are otherwise inaccessible to explicit DFT simulations.