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

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