ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

The Perturbed Chain Polar Statistical Associating Fluid Theory (PCP-SAFT) equation of state (EoS) is widely used to predict fluid-phase thermodynamics, but parameterization of PCP-SAFT for individual molecules is often challenging. We propose a machine learning framework called ML-SAFT for predicting parameters of PCP-SAFT. In order to provide data for training machine learning models, we created the largest dataset of regressed PCP-SAFT parameters in the literature. We then conducted extensive evaluation of several machine learning architectures for predicting PCP-SAFT parameters. We found that our best model provided accurate predictions for a wider range of molecules than existing predictive techniques with 39 \% average absolute deviation (AAD) in vapor pressure predictions and 9 \% AAD in density predictions.