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Article Dans Une Revue Physical Chemistry Chemical Physics Année : 2022

Thermal fluctuation and conformational effects on NMR parameters in β-O-4 lignin dimers from QM/MM and machine-learning approaches

Résumé

Advanced solid-state and liquid-state nuclear magnetic resonance (NMR) approaches have enabled high throughput information about functional groups and types of bonding in a variety of lignin fragments from degradation processes and laboratory synthesis. The use of quantum chemical (QM) methods may provide detailed insight into the relationships between NMR parameters and specific lignin conformations and their dynamics, whereas a rapid prediction of NMR properties could be achieved by combining QM with machine-learning (ML) approaches. In this study, we present the effect of conformations of β-O-4 linked lignin guaiacyl dimers on 13C and 1H chemical shifts while considering the thermal fluctuations of the guaiacyl dimers in water, ethanol and acetonitrile, as well as their binary 75 wt% aqueous solutions. Molecular dynamics and QM/MM simulations were used to describe the dynamics of guaiacyl dimers. The isotropic shielding of the majority of the carbon nuclei was found to be less sensitive toward a specific conformation than that of the hydrogen nuclei. The largest 1H downfield shifts of 4–6 ppm were established in the hydroxy groups and the rings in the presence of organic solvent components. The Gradient Boosting Regressor model has been trained on 60% of the chemical environments in the dynamics trajectories with the NMR isotropic shielding (σiso), computed with density-functional theory, for lignin atoms. The high efficiency of this machine-learning model in predicting the remaining 40% σiso(13C) and σiso(1H) values was established.
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Dates et versions

hal-03645084 , version 1 (10-08-2022)

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Sonia Milena Aguilera-Segura, Dominik Dragún, Robin Gaumard, Francesco Di Renzo, Irina Malkin Ondík, et al.. Thermal fluctuation and conformational effects on NMR parameters in β-O-4 lignin dimers from QM/MM and machine-learning approaches. Physical Chemistry Chemical Physics, 2022, 24 (15), pp.8820-8831. ⟨10.1039/d2cp00361a⟩. ⟨hal-03645084⟩
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