Unraveling pyrolysis mechanisms of lignin dimer model compounds: Neural network-based molecular dynamics simulation investigations

Abstract

Unraveling the pyrolysis mechanisms of lignin is of great importance for effective lignin utilization. However, both conventional quantum mechanical method and molecular dynamics (MD) simulations based on empirical potentials, e.g., the ReaxFF, cannot guarantee simultaneous accuracy and efficiency in exploring lignin pyrolysis mechanisms. Here, we developed a neural network (NN) force field for four types of representative lignin dimers based on 56,164 structures at the level of density functional theory (DFT). Such NN model was demonstrated to possess more accurate predictive power in both energetic and atomistic force properties than ReaxFF and was far more efficient than QM method. The MD simulations with the NN model showed that entire pyrolysis process can be divided into three stages dependent on the reaction temperature: homolytic cleavage of C–C bond and C-O bond, the changes from methoxy group to hydroxy, methyl, and formyl group, ring-opening reactions and the ring structure evolutions, and some novel reactions. The NN-based MD has provided us an atomic-level understanding of lignin pyrolysis and enabled us to build a comprehensive lignin pyrolysis network by means of the reactions disclosed based on the NN-based MD, which provided a useful guide for taking advantage of lignin more effectively.