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

Journal: Journal of chemical information and modeling
Published Date: Sep 1, 2020

Abstract

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.

Authors

  • Julia Gebhardt
    Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, D-70569 Stuttgart, Germany.
  • Matthias Kiesel
    Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, D-70569 Stuttgart, Germany.
  • Sereina Riniker
    Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland.
  • Niels Hansen
    Department of Psychiatry and Psychotherapy (NH, HE, JW), University of Göttingen Medical Center, Göttingen, Germany.

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