用热力学约束的神经网络，精准预测少数据下的纯物质汽液平衡参数。
Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
- 将克劳修斯-克拉佩龙方程融入图神经网络，多任务联合建模汽液性质。
- 在数据稀缺时性能提升显著，尤其对低样本属性预测精度更高。
- 适合化工中实验数据不足场景，兼顾准确性与物理一致性。
机器学习在预测化学过程设计相关的分子性质方面展现出良好前景，但常受限于实验数据稀少及热力学不一致问题。为此，研究提出热力学约束的机器学习方法，即在损失函数中引入热力学关系作为正则化项。本文将热力学约束的图神经网络思想从吉布斯-杜安方程迁移至克劳修斯-克拉佩龙方程，以多任务方式同时预测纯组分的蒸气压、液相摩尔体积、气相摩尔体积和汽化焓。相比单任务学习，克劳修斯-GNN模型在预测精度上表现更优；相较于纯粹数据驱动的多任务学习，其对克劳修斯-克拉佩龙方程的逼近能力也显著提升。尤其在数据稀缺情况下，预测精度提升最为明显，表明该模型在化工实践中数据匮乏场景中具有应用潜力。
原文摘要 · Abstract (English)
Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce experimental property data and lack thermodynamic consistency. As such, thermodynamics-informed ML, i.e., incorporating thermodynamic relations into the loss function as regularization term for training, has been proposed. We herein transfer the concept of thermodynamics-informed graph neural networks (GNNs) from the Gibbs-Duhem to the Clapeyron equation, predicting several pure component properties in a multi-task manner, namely: vapor pressure, liquid molar volume, vapor molar volume and enthalpy of vaporization. We find improved prediction accuracy of the Clapeyron-GNN compared to the single-task learning setting, and improved approximation of the Clapeyron equation compared to the purely data-driven multi-task learning setting. In fact, we observe the largest improvement in prediction accuracy for the properties with the lowest availability of data, making our model promising for practical application in data scarce scenarios of chemical engineering practice.
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