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[2509.06484] Thermodynamically consistent machine learning model for excess Gibbs energy
Summary
The paper presents HANNA, a machine learning model designed to predict excess Gibbs energy in multi-component mixtures, integrating physical laws to ensure thermodynamic consistency.
Why It Matters
This research addresses a significant challenge in chemical engineering by providing a reliable method for predicting thermodynamic properties, which is crucial for various applications in chemistry and materials science. The model's open-source nature promotes accessibility and further research in the field.
Key Takeaways
- HANNA integrates physical laws as constraints for accurate predictions.
- The model is trained on diverse experimental data, enhancing its applicability.
- It outperforms existing benchmark methods in predicting excess Gibbs energy.
Computer Science > Machine Learning arXiv:2509.06484 (cs) [Submitted on 8 Sep 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Thermodynamically consistent machine learning model for excess Gibbs energy Authors:Marco Hoffmann, Thomas Specht, Quirin Göttl, Jakob Burger, Stephan Mandt, Hans Hasse, Fabian Jirasek View a PDF of the paper titled Thermodynamically consistent machine learning model for excess Gibbs energy, by Marco Hoffmann and 6 other authors View PDF HTML (experimental) Abstract:The excess Gibbs energy plays a central role in chemical engineering and chemistry, providing a basis for modeling thermodynamic properties of liquid mixtures. Predicting the excess Gibbs energy of multi-component mixtures solely from molecular structures is a long-standing challenge. We address this challenge with HANNA, a flexible machine learning model for excess Gibbs energy that integrates physical laws as hard constraints, guaranteeing thermodynamically consistent predictions. HANNA is trained on experimental data for vapor-liquid equilibria, liquid-liquid equilibria, activity coefficients at infinite dilution and excess enthalpies in binary mixtures. The end-to-end training on liquid-liquid equilibrium data is facilitated by a surrogate solver. A geometric projection method enables robust extrapolations to multi-component mixtures. We demonstrate that HANNA delivers accurate predictions, while providing a substantially broader domain of applicability than state-of-th...