用图神经网络增强简单模型，预测液体蒸气平衡
Graph Neural Networks embedded into Margules model for vapor-liquid equilibria prediction
- 将GNN嵌入扩展的Margules模型，利用无限稀释数据训练
- 在多种二元混合物中表现优于传统UNIFAC模型
- 适合缺乏参数或难分解分子的体系，提供新预测思路
预测热力学模型对产品与工艺设计的早期阶段至关重要。本文分析了将图神经网络（GNN）嵌入相对简单的过量吉布斯能模型——扩展的Margules模型，在预测汽液平衡（VLE）方面的性能。通过与成熟的UNIFAC-Dortmund模型对比发现，整体精度略低，但在多种类型的二元混合物中表现出更高精度。由于基于基团贡献的方法（如UNIFAC）受限于分子碎片化可行性或参数可用性，本研究提出的GNN-Margules模型为VLE估算提供了替代方案。研究结果确立了仅使用无限稀释数据训练的简单过量吉布斯能模型结合GNN所能达到的预测精度基准。
原文摘要 · Abstract (English)
Predictive thermodynamic models are crucial for the early stages of product and process design. In this paper the performance of Graph Neural Networks (GNNs) embedded into a relatively simple excess Gibbs energy model, the extended Margules model, for predicting vapor-liquid equilibrium is analyzed. By comparing its performance against the established UNIFAC-Dortmund model it has been shown that GNNs embedded in Margules achieves an overall lower accuracy. However, higher accuracy is observed in the case of various types of binary mixtures. Moreover, since group contribution methods, like UNIFAC, are limited due to feasibility of molecular fragmentation or availability of parameters, the GNN in Margules model offers an alternative for VLE estimation. The findings establish a baseline for the predictive accuracy that simple excess Gibbs energy models combined with GNNs trained solely on infinite dilution data can achieve.
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