  A major bottleneck in developing sustainable processes and materials is a
lack of property data. Recently, machine learning approaches have vastly
improved previous methods for predicting molecular properties. However, these
machine learning models are often not able to handle thermodynamic constraints
adequately. In this work, we present a machine learning model based on natural
language processing to predict pure-component parameters for the
perturbed-chain statistical associating fluid theory (PC-SAFT) equation of
state. The model is based on our previously proposed
SMILES-to-Properties-Transformer (SPT). By incorporating PC-SAFT into the
neural network architecture, the machine learning model is trained directly on
experimental vapor pressure and liquid density data. Combining established
physical modeling approaches with state-of-the-art machine learning methods
enables high-accuracy predictions across a wide range of pressures and
temperatures, while maintaining the physical meaning of PC-SAFT parameters.
SPT-PCSAFT demonstrates exceptional prediction accuracy even for complex
molecules with various functional groups, outperforming traditional group
contribution methods by a factor of four in the mean average percentage
deviation. Moreover, SPT-PCSAFT captures the behavior of stereoisomers without
any special consideration. To facilitate the application of our model, we
provide predicted PC-SAFT parameters of more than 13645 components, making
PC-SAFT accessible to all researchers.
