GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures

Abstract

Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we have developed GRAPPA - a hybrid graph neural network for predicting vapor pressures of pure components. GRAPPA enables the prediction of the vapor pressure curve of basically any organic molecule, requiring only the molecular structure as input. The new model consists of three parts: A graph attention network for the message passing step, a pooling function that captures long-range interactions, and a prediction head that yields the component-specific parameters of the Antoine equation, from which the vapor pressure can readily and consistently be calculated for any temperature. We have trained and evaluated GRAPPA on experimental vapor pressure data of almost 25,000 pure components. We found excellent prediction accuracy for unseen components, outperforming state-of-the-art group contribution methods and other machine learning approaches in applicability and accuracy. The trained model and its code are fully disclosed, and GRAPPA is directly applicable via the interactive website ml-prop.mv.rptu.de.

38 pages, 12 figures

References in corpus (8)

• Decoupled Weight Decay Regularization
• Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
• Accurate Learning of Graph Representations with Graph Multiset Pooling
• Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
• HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
• Gibbs-Helmholtz Graph Neural Network: capturing the temperature dependency of activity coefficients at infinite dilution
• Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
• Understanding the language of molecules: Predicting pure component parameters for the PC-SAFT equation of state from SMILES

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