All models available on MLPROP are developed by the Laboratory of Engineering Thermodynamics (LTD) at RPTU Kaiserslautern and require the molecular structure in the form of a SMILES as input. Below is a list of the currently available models on MLPROP with a brief description and a link to the corresponding publication.

GRAPPA - Graph Neural Network for Predicting the Parameters of the Antoine Equation

  • Vapor pressure and boiling point prediction
  • Based on the Antoine equation:
    • \(\ln(p^{\mathrm{s}} / \mathrm{kPa}) = A - \frac{B}{T / \mathrm{K} + C}\)
  • Paper in Chemical Engineering Journal Advances
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UNIFAC 2.0 and mod. UNIFAC 2.0

  • Combining machine learning with classical group-contribution
  • Missing parameters predicted with matrix completion
  • Higher prediction accuracy than the original methods
  • UNIFAC 2.0 paper, mod. UNIFAC 2.0 pre-print
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HANNA - Hard-constraint Neural Network for Consistent Activity Coefficient Prediction

  • Prediction of binary activity coefficients
  • Strict thermodynamic consistency
  • Paper available in Chemical Science
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Prediction of Vapor-Liquid Equilibria

  • Prediction of isothermal and isobaric binary VLE
  • Vapor pressure calculated by GRAPPA
  • Activity coefficients by \(g^\mathrm{E}\) model of choice
  • Based on extended Raoult's law:
    • \(p_i^{\mathrm{s}}\,x_i\,\gamma_i= p\, y_i\)
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