用图神经网络预测任意有机分子的蒸气压，仅需分子结构输入。
GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures
- 融合图注意力与池化机制，捕获分子局部与长程特征。
- 在近2.5万种化合物上训练，对未见分子预测准确率领先现有方法。
- 开源模型与交互网站可直接使用，适合化工研发人员快速估算蒸气压。
纯组分蒸气压是化学过程设计中最关键的性质之一，但迄今尚无通用、高精度且开源的预测方法。为此，我们开发了GRAPPA——一种用于预测纯组分蒸气压的混合图神经网络。该模型仅需分子结构作为输入，即可预测几乎所有有机分子的蒸气压曲线。GRAPPA由三部分构成：用于消息传递的图注意力网络、捕捉长程相互作用的池化函数，以及输出安托万方程参数的预测头，从而可在任意温度下一致计算蒸气压。模型在近25,000种纯组分的实验蒸气压数据上进行训练与评估，对未见分子表现出优异的预测精度，优于当前最优的基团贡献法及其他机器学习方法。训练好的模型及代码已全部公开，可通过交互式网站 ml-prop.mv.rptu.de 直接调用。
原文摘要 · Abstract (English)
Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we have developed GRAPPA - a hybrid graph neural network for predicting vapor pressures of pure components. GRAPPA enables the prediction of the vapor pressure curve of basically any organic molecule, requiring only the molecular structure as input. The new model consists of three parts: A graph attention network for the message passing step, a pooling function that captures long-range interactions, and a prediction head that yields the component-specific parameters of the Antoine equation, from which the vapor pressure can readily and consistently be calculated for any temperature. We have trained and evaluated GRAPPA on experimental vapor pressure data of almost 25,000 pure components. We found excellent prediction accuracy for unseen components, outperforming state-of-the-art group contribution methods and other machine learning approaches in applicability and accuracy. The trained model and its code are fully disclosed, and GRAPPA is directly applicable via the interactive website ml-prop.mv.rptu.de.
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