High-Throughput Application and Evaluation of the COSMO-SAC Model for Predictions of Liquid--Liquid Equilibria

Abstract

The predictive power of the COSMO-SAC activity coefficient model is rigorously tested using an extensive dataset of binary liquid–liquid equilibria (LLE). Two model variants, COSMO-SAC-2010 and COSMO-SAC-dsp, are evaluated across 2,478 binary systems and nearly 75,000 experimental data points. They achieve a success rate exceeding 90% in detecting the occurrence of LLE, demonstrating strong qualitative performance across chemically diverse systems. COSMO-SAC achieves the lowest deviations across a range of benchmark datasets and outperforms classical predictive models such as UNIFAC and COSMO-RS, confirming its state-of-the-art accuracy for LLE prediction. Among the two variants, COSMO-SAC-2010 yields more accurate quantitative predictions, while COSMO-SAC-dsp provides broader coverage, particularly for polar and hydrogen-bonding systems. Both variants reliably capture systematic trends across homologous series, making them effective tools for solvent screening and thermodynamic consistency analysis. A high-throughput and fully automated computational framework—integrated into the open-source package ThermoSAC—enables adaptive Gibbs energy screening, LLE tracing, and anomaly detection. This work establishes COSMO-SAC as a leading framework for predictive thermodynamics and offers reproducible benchmarks and tools for future model development.