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COSMOSol: efficient solvent screening for polymer additives with open-source COSMO-SAC
Adam Bouz1, Juraj Kosek1, Martin Klajmon1
1Faculty of Chemical Engineering, University of Chemistry and Technology, Prague Technická 5 166 28 Prague 6 Czechia adam.bouz@vscht.cz.
RSC Advances
|July 22, 2026
Summary
This study introduces a computational method using COSMO-SAC to predict polymer additive solubility in solvents, aiding efficient recycling. The approach accurately ranks solvent effectiveness, even without experimental data, offering a practical route for material science.
Area of Science:
- Materials Science
- Computational Chemistry
- Chemical Engineering
Background:
- Efficient polymer recycling necessitates effective removal of additives.
- Experimental solubility data for industrial additives and solvents are scarce.
- Predicting solvent-additive interactions is crucial for process optimization.
Purpose of the Study:
- To develop and validate a systematic computational approach for screening solvents for polymer additive dissolution.
- To assess the accuracy and robustness of the COSMO-SAC model for predicting additive solubility.
- To provide a practical, first-principles tool for solvent selection in recycling and other applications.
Main Methods:
- Utilized the quantum mechanics-aided COSMO-SAC model for solvent screening.
- Compiled experimental solubility data for five representative additives across diverse solvent classes for validation.
- Investigated the impact of various modeling choices (fusion properties, molecular conformation, etc.) on prediction accuracy.
- Benchmarked COSMO-SAC against a machine-learning model (HANNA).
Main Results:
- COSMO-SAC predictions showed quantitative agreement with experimental solubility data (within 0.5 log units).
- The model qualitatively identified optimal and suboptimal solvents for each additive.
- Rankings remained robust for distinguishing between solvent classes, even with limited empirical input.
- COSMO-SAC and HANNA yielded comparable solvent rankings.
Conclusions:
- The proposed computational approach offers a practical and accurate method for solvent screening in polymer additive dissolution.
- The COSMO-SAC model, with minimal empirical input, provides reliable predictions for solvent selection.
- The developed Python toolset (COSMOSol) facilitates reproducibility and further research in this area.
