Predicting Surfactant Oil-Water Interfacial Tension Using Gated Message-Passing Graph Neural Networks.

Using surfactants to manipulate the interfacial tension (IFT) of oil-water systems represent a critical strategy for enhanced oil recovery (EOR). However, predicting the physical properties of surfactants based on their molecular structure remains a challenging task, as conventional machine learning methods struggle to capture the coupled interactions between molecular structures and environmental parameters, while existing graph neural networks predominantly focus on single-molecule representations and overlook the characteristics of the system environment. Accordingly, a Gated Message-passing Graph Neural Network with an Attention Mechanism (Gated-MPNN-AT) is proposed to integrate molecular graph structures and environmental features, aiming to achieve accurate prediction of interfacial tension in surfactant-oil-water systems. The model dynamically controls the message passing process through a dual gated mechanism, adopts a Cross-Attention mechanism to achieve the in-depth interaction between molecular topological features and environmental parameters, and designs a hybrid robust loss function to handle the IFT data with cross-order-of-magnitude distribution. The research results show that the prediction accuracy of the model is better than that of traditional machine learning methods (such as Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)) and some graph neural network methods (such as Graph Convolutional Network (GCN), and Graph Attention Network (GAT)). Ablation experiments have confirmed that the gated mechanism increases the coefficient of determination (R 2) by 4.8%, and the Cross-Attention fusion strategy reduces the mean absolute error (MAE) by 21.3%. Meanwhile, the model has good generalization ability and strong anti-interference ability against abnormal IFT data.