Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning

Abstract

A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Finally, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.