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

UNIFAC 2.0 and mod. UNIFAC 2.0

HANNA - Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
- Model for the excess Gibbs energy \(g^\mathrm{E}\)
- Strictly thermodynamically consistent
- New version trained to VLE and LLE data
- New version applicable to multi-component mixtures
- Latest pre-print available

Prediction of Binary and Ternary Phase Equilibria using the CEM
- Prediction of binary and ternary phase diagrams
- Simultaneous calculation of VLE and LLE
- Activity coefficients supplied by HANNA
- Applicable to multi-component mixtures
- Based on the convex envelope method (CEM)

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: \(p_i^{\mathrm{s}}\,x_i\,\gamma_i= p\, y_i\)
