{"ID":2873348,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06484","arxiv_id":"2509.06484","title":"Thermodynamically consistent machine learning model for excess Gibbs energy","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.","short_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 HAN...","url_abs":"https://arxiv.org/abs/2509.06484","url_pdf":"https://arxiv.org/pdf/2509.06484v2","authors":"[\"Marco Hoffmann\",\"Thomas Specht\",\"Quirin Göttl\",\"Jakob Burger\",\"Stephan Mandt\",\"Hans Hasse\",\"Fabian Jirasek\"]","published":"2025-09-08T09:47:03Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CE\"]","methods":"[]","has_code":false}