  The availability of property data is one of the major bottlenecks in the
development of chemical processes, often requiring time-consuming and expensive
experiments or limiting the design space to a small number of known molecules.
This bottleneck has been the motivation behind the continuing development of
predictive property models. For the property prediction of novel molecules,
group contribution methods have been groundbreaking. In recent times, machine
learning has joined the more established property prediction models. However,
even with recent successes, the integration of physical constraints into
machine learning models remains challenging. Physical constraints are vital to
many thermodynamic properties, such as the Gibbs-Duhem relation, introducing an
additional layer of complexity into the prediction. Here, we introduce
SPT-NRTL, a machine learning model to predict thermodynamically consistent
activity coefficients and provide NRTL parameters for easy use in process
simulations. The results show that SPT-NRTL achieves higher accuracy than
UNIFAC in the prediction of activity coefficients across all functional groups
and is able to predict many vapor-liquid-equilibria with near experimental
accuracy, as illustrated for the exemplary mixtures water/ethanol and
chloroform/n-hexane. To ease the application of SPT-NRTL, NRTL-parameters of
100 000 000 mixtures are calculated with SPT-NRTL and provided online.
