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NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule
| Title: | NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule |
| Authors: | Tsitsvero, Mikhail Browse this author | | Pirillo, Jenny Browse this author | | Hijikata, Yuh Browse this author →KAKEN DB | | Komatsuzaki, Tamiki Browse this author →KAKEN DB |
| Keywords: | Density functional theory | | Molecular dynamics | | Artificial neural networks | | Machine learning | | Computational methods | | Nuclear magnetic resonance spectroscopy | | Covariance and correlation | | Gaussian processes |
| Issue Date: | 17-May-2023 |
| Publisher: | AIP Publishing |
| Journal Title: | Journal of chemical physics |
| Volume: | 158 |
| Issue: | 19 |
| Start Page: | 194108 |
| Publisher DOI: | 10.1063/5.0147398 |
| Abstract: | Nuclear magnetic resonance (NMR) spectroscopy is one of the indispensable techniques in chemistry because it enables us to obtain accurate information on the chemical, electronic, and dynamic properties of molecules. Computational simulation of the NMR spectra requires time-consuming density functional theory (DFT) calculations for an ensemble of molecular conformations. For large flexible molecules, it is considered too high-cost since it requires time-averaging of the instantaneous chemical shifts of each nuclear spin across the conformational space of molecules for NMR timescales. Here, we present a Gaussian process/deep kernel learning-based machine learning (ML) method for enabling us to predict, average in time, and analyze the instantaneous chemical shifts of conformations in the molecular dynamics trajectory. We demonstrate the use of the method by computing the averaged H-1 and C-13 chemical shifts of each nuclear spin of a trefoil knot molecule consisting of 24 para-connected benzene rings (240 atoms). By training ML model with the chemical shift data obtained from DFT calculations, we predicted chemical shifts for each conformation during dynamics. We were able to observe the merging of the time-averaged chemical shifts of each nuclear spin in a singlet H-1 NMR peak and two C-13 NMR peaks for the knot molecule, in agreement with experimental measurements. The unique feature of the presented method is the use of the learned low-dimensional deep kernel representation of local spin environments for comparing and analyzing the local chemical environment histories of spins during dynamics. It allowed us to identify two groups of protons in the knot molecule, which implies that the observed singlet H-1 NMR peak could be composed of the contributions from protons with two distinct local chemical environments. |
| Rights: | This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Mikhail Tsitsvero, Jenny Pirillo, Yuh Hijikata, Tamiki Komatsuzaki; NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule. J. Chem. Phys. 15 May 2023; 158 (19): 194108. and may be found at https://doi.org/10.1063/5.0147398 |
| Type: | article |
| URI: | http://hdl.handle.net/2115/92421 |
| Appears in Collections: | 化学反応創成研究拠点：ICReDD (Institute for Chemical Reaction Design and Discovery : ICReDD) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: Mikhail Tsitsvero
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