Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks

arXiv:2403.03767v1 Announce Type: cross
Abstract: The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in industry. Recently, classical QSPR and Graph Neural Networks (GNNs), a deep learning technique, have been successfully applied to predict the CMC of surfactants at room temperature. However, these models have not yet considered the temperature dependency of the CMC, which is highly relevant for practical applications. We herein develop a GNN model for temperature-dependent CMC prediction of surfactants. We …

abstract applications arxiv cs.lg deep learning gnns graph graph neural networks however industry molecules networks neural networks physics.chem-ph property room type