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[2602.18313] Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
Summary
The paper presents a novel approach using thermodynamics-informed graph neural networks to predict vapor-liquid equilibrium properties, improving accuracy in data-scarce scenarios.
Why It Matters
This research addresses the challenge of limited experimental data in chemical process design by integrating thermodynamic principles into machine learning models. It enhances prediction accuracy for critical properties, making it relevant for chemical engineers and researchers focused on sustainable process design.
Key Takeaways
- Introduces thermodynamics-informed graph neural networks for predicting vapor-liquid equilibrium properties.
- Demonstrates improved accuracy in predictions, particularly for properties with limited data availability.
- Highlights the practical applications of the model in chemical engineering under data-scarce conditions.
Physics > Chemical Physics arXiv:2602.18313 (physics) [Submitted on 20 Feb 2026] Title:Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria Authors:Jan Pavšek, Alexander Mitsos, Elvis J. Sim, Jan G. Rittig View a PDF of the paper titled Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria, by Jan Pav\v{s}ek and Alexander Mitsos and Elvis J. Sim and Jan G. Rittig View PDF HTML (experimental) Abstract:Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce experimental property data and lack thermodynamic consistency. As such, thermodynamics-informed ML, i.e., incorporating thermodynamic relations into the loss function as regularization term for training, has been proposed. We herein transfer the concept of thermodynamics-informed graph neural networks (GNNs) from the Gibbs-Duhem to the Clapeyron equation, predicting several pure component properties in a multi-task manner, namely: vapor pressure, liquid molar volume, vapor molar volume and enthalpy of vaporization. We find improved prediction accuracy of the Clapeyron-GNN compared to the single-task learning setting, and improved approximation of the Clapeyron equation compared to the purely data-driven multi-task learning setting. In fact, we observe the largest improvement in prediction accuracy for the properties with the lowest availability of data, making our model p...