Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

Area of Science:

• Computational Chemistry
• Physical Chemistry
• Materials Science

Background:

• Predicting critical micelle concentration (CMC) is crucial for surfactant applications.
• Existing models often struggle with diverse surfactant chemistries and uncertainty quantification.
• Machine learning offers potential for improved CMC prediction.

Purpose of the Study:

• To develop a novel computational model for predicting CMCs.
• To incorporate uncertainty estimation into CMC predictions.
• To compare the performance of the novel model against established methods.

Main Methods:

• Utilized graph neural networks (GNNs) integrated with Gaussian processes (GPs).
• Employed learned latent space representations of molecules for prediction.
• Evaluated the model on a dataset encompassing nonionic, cationic, anionic, and zwitterionic surfactants.
• Compared performance against a linear model using extended connectivity fingerprints (ECFPs).

Main Results:

• The GNN-based model demonstrated slightly superior performance over the linear ECFP model with sufficient, balanced training data.
• Achieved predictive accuracy comparable to existing models across a broader range of surfactant chemistries.
• Successfully visualized the model's applicability domain using a molecular cartogram to identify potential prediction errors.
• Provided insights into molecular properties influencing CMC values.

Conclusions:

• The proposed GNN-GP model offers a robust approach for CMC prediction and uncertainty quantification.
• The model's ability to handle diverse surfactant types enhances its practical applicability.
• The visualization tool aids in understanding model limitations and guiding future research.