Chemical Shifts in Molecular Solids by Machine Learning

TL;DR

This paper introduces a machine learning approach to accurately predict chemical shifts in molecular solids, enabling structure elucidation with near first-principles accuracy and assisting in NMR crystallography.

Contribution

The authors develop a local environment-based machine learning model that predicts chemical shifts in molecular solids with high accuracy, addressing the challenge of vast chemical space.

Findings

Achieved DFT-level accuracy in chemical shift predictions (RMSE: 0.49 ppm for 1H, 4.3 ppm for 13C).

Successfully identified structures of cocaine and other compounds using ML-predicted shifts.

Model demonstrated high correlation (R^2 > 0.97) across multiple nuclei.

Abstract

The calculation of chemical shifts in solids has enabled methods to determine crystal structures in powders. The dependence of chemical shifts on local atomic environments sets them among the most powerful tools for structure elucidation of powdered solids or amorphous materials. Unfortunately, this dependency comes with the cost of high accuracy first-principle calculations to qualitatively predict chemical shifts in solids. Machine learning methods have recently emerged as a way to overcome the need for explicit high accuracy first-principle calculations. However, the vast chemical and combinatorial space spanned by molecular solids, together with the strong dependency of chemical shifts of atoms on their environment, poses a huge challenge for any machine learning method. Here we propose a machine learning method based on local environments to accurately predict chemical shifts of different molecular solids and of different polymorphs within DFT accuracy (RMSE of 0.49 ppm ( 1 H), 4.3ppm ( 13 C), 13.3 ppm ( 15 N), and 17.7 ppm ( 17 O) with $R^2$ of 0.97 for 1 H, 0.99 for 13 C, 0.99 for 15 N, and 0.99 for 17 O). We also demonstrate that the trained model is able to correctly determine, based on the match between experimentally-measured and ML-predicted shifts, structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid in an chemical shift based NMR crystallography approach.