用物理定律约束机器学习，精准预测多组分液体混合物的吉布斯过量能。
Thermodynamically consistent machine learning model for excess Gibbs energy
- 将热力学规律作为硬约束融入模型，确保预测结果物理自洽。
- 在二元体系实验数据上训练，对多组分体系实现高精度外推。
- 开源模型与交互界面，适合化工与材料领域的研究人员使用。
过量吉布斯能是化学工程和化学中的核心概念，用于建模液态混合物的热力学性质。仅从分子结构预测多组分混合物的过量吉布斯能是一项长期挑战。本文提出HANNA，一种融合物理定律的灵活机器学习模型，可保证热力学一致性预测。该模型基于气液平衡、液液平衡、无限稀释活度系数及二元混合物过量焓的实验数据进行端到端训练，利用代理求解器实现液液平衡数据的高效训练。通过几何投影方法，模型可稳健外推至多组分体系。实验表明，HANNA在预测精度上优于现有基准方法，并显著扩展了适用范围。训练好的模型及代码已公开，配套交互界面可在MLPROP网站获取。
原文摘要 · Abstract (English)
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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