Predicting the Solubility of Lignin via Machine Learning

Lignin is a highly promising renewable resource, but its practical application faces challenges due to its polydispersity and variability in solubility. This study utilized real-world characterization data (gel permeation chromatography (GPC) and HSQC NMR) to construct the molecular structures of 100 lignins of varying molecular weights. We used a machine learning (ML) approach, combining structural features with quantum chemical information, to predict the solubilities of these lignins in various solvents (calculated using COSMOtherm software). The machine learning model demonstrated high accuracy (R2 values of 0.987, 0.892, and 0.970, respectively), demonstrating its effectiveness in predicting lignin solubility based on structure and solvent properties. Furthermore, SHAP analysis elucidated the influence of individual molecular features on solubility predictions, contributing to our understanding of how the lignin structure influences solubility. This study provides valuable insights into the selection of highly soluble green solvents and the preparation of monodisperse lignin.