Improved Solubility Predictions in scCO2 Using Thermodynamics-Informed Machine Learning Models

Journal of Chemical Information and Modeling. 2025. Vol. 65. No. 8. P. 4043–4056.

Makarov D. M., Kalikin N., Budkov Y., Gurikov P., Kruchinin S. E., Jouyban A., Kiselev M. G.

Accurate solubility prediction in supercritical carbon dioxide (scCO2) is crucial for optimizing experimental
design by eliminating unnecessary and costly trials at an early stage, thereby streamlining the workflow. A comprehensive solubility database containing 31,975 records has been compiled, providing a foundation for developing predictive models applicable to a diverse class of chemical compounds, with a particular focus on drug-like
substances. In this study, we propose a domain-aware machine learning approach that incorporates thermodynamic properties governing phase transitions to solubility predictions in scCO2. Predictive models were developed using the CatBoost algorithm and a graph-based architecture employing directed message passing to identify the most effective approach. Furthermore, auxiliary properties of the solute, including melting point, critical parameters, enthalpy of vaporization, and Gibbs free energy of solvation, were predicted as part of this work. The findings underscore
the efficacy of incorporating domain-specific thermodynamic features to enhance the predictive accuracy of scCO2 solubility modeling. The interpretation and the applicability domain assessment have confirmed the qualitative selection of the employed descriptors, demonstrating their ability to generalize to unique compounds that fall outside the defined domain.

Keywords: сверхкритические флюиды, Machine learning algorithms, Solubility, Supercritical fluids, машинное обучение на графах, растворимость в сверхкритическом CO2

Publication based on the results of: Прогнозирование свойств молекулярных систем: совмещение методов машинного обучения и классических методов моделирования (2026)