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arXiv · 2509.06484

Thermodynamically consistent machine learning model for excess Gibbs energy

Marco Hoffmann·Thomas Specht·Quirin Göttl·Jakob Burger·Stephan Mandt·Hans Hasse·Fabian Jirasek
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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-the-art benchmark methods. The trained model and corresponding code are openly available, and an interactive interface is provided on our website, MLPROP.

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Marco Hoffmann, Thomas Specht, Quirin Göttl, Jakob Burger, Stephan Mandt, Hans Hasse, Fabian Jirasek. 2026-02-20. Thermodynamically consistent machine learning model for excess Gibbs energy. https://doi.org/10.1038/s41467-026-71430-y

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