Physics

Solvation

Relevance (law)

Activity coefficient

Organic chemistry

UNIFAC

Work (physics)

Limiting

Pure mathematics

Mathematics

Statistical physics

Force field (fiction)

Machine learning

Engineering

Political science

Computational chemistry

Physical chemistry

Artificial intelligence

Aqueous solution

Chemistry

Molecule

Computer science

Molecular dynamics

Field (mathematics)

Mechanical engineering

Thermodynamics

Journal of Chemical Information and Modeling

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.

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