Predicting Critical Micelle Concentrations for Surfactants Using Graph Convolutional Neural Networks | AIChE

Predicting Critical Micelle Concentrations for Surfactants Using Graph Convolutional Neural Networks


Conference Presentation

Conference Type

AIChE Annual Meeting

Presentation Date

November 8, 2021


12 minutes

Skill Level




Surfactants are amphiphilic molecules that are widely used in consumer products [1], industrial processes [2], and biological research [3,4]. A critical property of a surfactant is the critical micelle concentration (CMC), at which surfactant molecules undergo cooperative self-assembly in solution [5]. The formation of micelles in surfactant solution can bring significant changes in properties such as surface tension, electrical conductivity, light scattering, and reaction mechanism [5,6]. Consequently, predicting CMCs is important for surfactant selection and design in applications such as drug delivery [7] and detergents [8].

Traditionally, CMCs are determined experimentally through techniques such as tensiometry, but this method is laborious and expensive [9]. As an alternative to experiments, computational methods such as molecular dynamics (MD) simulations [10, 11, 12] and quantitative structure-property relationships (QSPR) that are based on molecular descriptors [13, 14, 15] have been used to predict CMCs. These approaches have been shown to predict CMCs with relatively high accuracy, but they have several limitations. For instance, MD simulations usually require high computational cost and assumptions regarding the number of surfactants within a micelle [10, 11, 12], whereas QSPR models are often applicable to only a single class of surfactant and may need density functional theory (DFT) calculations to obtain quantum-chemical descriptors [16].

Recent advances in machine learning methods for molecular property prediction can help overcome some of these obstacles, specifically graph convolutional neural networks (GCNs) [17], since chemicals can be intuitively represented as molecular graphs that are natural inputs for GCNs. A GCN architecture can handle input molecules with different sizes without the need for artificial data manipulations and approximates the physical interactions between atoms through graph convolutions that aggregate features of adjacent atoms [17].

In this work, we present a simple GCN that can predict CMCs directly from the molecular structure of a surfactant monomer and test the ability of the model to generalize to a dataset containing nonionic, anionic, cationic, and zwitterionic surfactants [5]. We also perform saliency analysis [18] to interpret how atom types and surfactant substructures contribute to CMCs and compare these results with the physical rules that correlate structural information of surfactants to CMCs [6]. Following such rules, we propose a small set of new surfactants for which experimental CMCs are not available; for these molecules, we show that CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations [19]. The findings of this research provide evidence that GCNs can be used for high-throughput screening of surfactants with desired self-assembly characteristics.


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[19] T. Jin, S.J. Patel, R.C. Van Lehn, Molecular simulations of lipid membrane partitioning and translocation by bacterial quorum sensing modulators, PLoS One. 16 (2021) e0246187.


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