Graph Neural Networks embedded into Margules model for vapor-liquid equilibria prediction

TL;DR

This paper explores embedding Graph Neural Networks into the Margules model to predict vapor-liquid equilibria, offering an alternative to traditional group contribution methods with promising results for binary mixtures.

Contribution

It introduces a novel approach combining GNNs with the Margules model for VLE prediction, providing a baseline for accuracy using only infinite dilution data.

Findings

GNN-embedded Margules model achieves lower overall accuracy than UNIFAC-Dortmund.

Higher accuracy observed for various binary mixtures with GNN-Margules.

GNN-Margules offers an alternative when group contribution methods are limited.

Abstract

Predictive thermodynamic models are crucial for the early stages of product and process design. In this paper the performance of Graph Neural Networks (GNNs) embedded into a relatively simple excess Gibbs energy model, the extended Margules model, for predicting vapor-liquid equilibrium is analyzed. By comparing its performance against the established UNIFAC-Dortmund model it has been shown that GNNs embedded in Margules achieves an overall lower accuracy. However, higher accuracy is observed in the case of various types of binary mixtures. Moreover, since group contribution methods, like UNIFAC, are limited due to feasibility of molecular fragmentation or availability of parameters, the GNN in Margules model offers an alternative for VLE estimation. The findings establish a baseline for the predictive accuracy that simple excess Gibbs energy models combined with GNNs trained solely on infinite dilution data can achieve.