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

ACS Omega 2026 Open access

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Authors: Suiyang Liu, Yanrong Cui, Yi-Xiang Wang, Hualei Xu, Houshun Jiang
DOI: 10.1021/acsomega.6c03262
Cited by: 1
References: 37

Abstract

) 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.

Fields of Study & Subjects

Enhanced Oil Recovery Techniques Pickering emulsions and particle stabilization Surfactants and Colloidal Systems

Publication details

Journal / source
ACS Omega
Publisher
American Chemical Society (ACS)
Volume
11
Issue
24
Pages
36101–36116
Publication date
8 June 2026
Language
EN