Modified UNIFAC 2.0-A Group-Contribution Method Completed with Machine Learning

Predicting thermodynamic properties of mixtures is a
cornerstone
of chemical engineering, yet conventional group-contribution (GC)
methods like modified UNIFAC (Dortmund) remain limited by incomplete
parameter tables. To address this, we present modified UNIFAC 2.0,
a hybrid model that integrates a matrix completion method from machine
learning into the GC framework, allowing for the simultaneous training
of all pair-interaction parameters, including those that cannot be
fitted directly due to missing data. By training on more than 500,000
experimental data points for activity coefficients and excess enthalpies
from the Dortmund Data Bank, modified UNIFAC 2.0 achieves improved
accuracy, while significantly expanding the predictive scope compared
to the latest published modified UNIFAC (Dortmund) version, which
covers only 39% of all possible interactions. Its flexible design
allows updates with new experimental data or customizations for specific
applications. The new model can easily be implemented in established
simulation software with complete parameter tables readily available.