Chemical shifts in molecular solids by machine learning

Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti () and Lyndon Emsley ()
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Federico M. Paruzzo: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Albert Hofstetter: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Félix Musil: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Sandip De: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Michele Ceriotti: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Lyndon Emsley: Ecole Polytechnique Fédérale de Lausanne (EPFL)

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the ability to calculate them, which comes at the cost of high accuracy first-principles calculations. Machine learning has recently emerged as a way to overcome the need for quantum chemical calculations, but for chemical shifts in solids it is hindered by the chemical and combinatorial space spanned by molecular solids, the strong dependency of chemical shifts on their environment, and the lack of an experimental database of shifts. We propose a machine learning method based on local environments to accurately predict chemical shifts of molecular solids and their polymorphs to within DFT accuracy. We also demonstrate that the trained model is able to determine, based on the match between experimentally measured and ML-predicted shifts, the structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid.

Date: 2018
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DOI: 10.1038/s41467-018-06972-x

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