Combining Molecular Dynamics and Machine Learning to Predict Self-Solvation Free Energies and Limiting Activity Coefficients

Computational prediction of limiting activity coefficients is of great relevance for process design. For highly nonideal mixtures including molecules with directed interactions, methods that maintain the molecular character of the solvent are most promising. Computational expense and force-field deficiencies are the main limiting factors that prevent the use of high-throughput molecular dynamics (MD) simulations in a predictive setup. The combination of MD simulations and machine learning used in this work accounts for both issues. Comparison to published data including free-energy simulations, COSMO-RS and UNIFAC models, reveals competitive prediction accuracy.