Dissolving pine lignin remains a significant challenge due to its complex, recalcitrant structure and high degree of crosslinking, making it less responsive to conventional solvents. Ionic liquids (ILs) have emerged as promising green solvents for lignin solubilization, but their structural diversity presents a bottleneck for rational design and optimization. Our research integrates computational screening, ML modeling, and experimental validation to systematically identify and optimize ILs that maximize lignin solubility while minimizing environmental impact. Initial COSMO-RS simulations predict IL properties such as partition coefficients and hydrogen-bonding interactions to identify promising IL candidates. These data train ML models using structural features of ILs to predict and rank new IL formulations for lignin extraction. The Random Forest algorithm demonstrated superior predictive performance (R² = 0.94) in modeling delignification efficiency. Feature importance analysis revealed that temperature (33%), anion halide content (15%), and anion oxygen count (12%) are the most influential factors affecting lignin dissolution. Imidazolium-based ILs, particularly [EMIM][OAc] and [BMIM][OAc], consistently showed high delignification performance, while cholinium-based ILs emerged as promising greener alternatives. Molecular dynamics simulations and quantum chemical calculations provided mechanistic insights into IL-lignin interactions, revealing that ILs with hydrophilic anions significantly lower the logarithmic activity coefficient (ln(γ)), thereby enhancing lignin solubility. This integrated approach represents a significant advancement in sustainable materials science, streamlining the discovery of high-performance, eco-friendly solvents for lignin valorization while contributing to circular bioeconomy development. Future work focuses on expanding the model to incorporate solvent recyclability and environmental impact metrics for comprehensive sustainable IL design.
