Machine learning-enabled screening and experimental validation of ionic liquids for highly efficient cellulose dissolution

Abstract

Ionic liquids (ILs) are widely utilized for cellulose dissolution due to their high structural tunability and exceptional capacity to disrupt hydrogen bond networks. However, the hard-to-predict cellulose solubility presents a significant challenge, and available models for this task remain limited. In this study, based on an up-to-date dataset of 329 cellulose solubility data points across 195 ILs, we first systematically evaluate the performance of COSMO-RS for cellulose-in-IL solubility prediction, which is selected as a representative of mechanism-driven methods and a baseline for the further development of data-driven methods. Subsequently, five statistical algorithms combined with two types of IL representations (namely Morgan fingerprints and RDKit descriptors) are rigorously trained and tested for the modelling of cellulose-in-IL solubility following the classical machine learning workflow. In addition, a Transformer–Convolutional Neural Network model is also extended to this task as an assessment of advanced deep learning frameworks. The best-performing model is employed to guide a large-scale screening of 1224 potential ILs. As a validation, five previously untested ILs are used to conduct temperature-dependent solubility measurements. This study provides a valuable tool for guiding IL-based cellulose preprocessing toward related material discovery.