Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution

Hypothesis: Predicting the surface tension (SFT)-log(c) profiles of hydrocarbon surfactants in aqueous solution is computationally non-trivial, and empirically challenging due to the diverse and complex architecture and interactions of surfactant molecules. Machine learning (ML), combining a data-based and knowledge-based approach, can provide a powerful means to relate molecular descriptors to SFT profiles. Experiments: A dataset of SFT for 154 model hydrocarbon surfactants at 20-30 degrees C is fitted to the Szyszkowski equation to extract three characteristic parameters (gamma(max),K-L and critical micelle concentration (CMC)) which are correlated to a series of 2D and 3D molecular descriptors. Key (similar to 10) descriptors were selected by removing co-correlation, and employing a gradient-boosted regressor model to rank feature importance and carry out recursive feature elimination (RFE). The hyperparameters of each target-variable model were fine-tuned using a randomised cross-validated grid search, to improve predictive ability and reduce overfitting. Findings: The ML models correlate favourably with test experimental data, with R-2= 0.69-0.87, and the merits and limitations of the approach are discussed based on 'unseen' hydrocarbon surfactants. The incorporation of a knowledge-based framework provides an appropriate smoothing of the experimental data which simplifies the data-driven approach and enhances its generality. Open-source codes and a brief tutorial are provided. (C) 2022 The Authors. Published by Elsevier Inc.

Automated summary

This study uses machine learning to relate molecular descriptors to the surface tension profiles of hydrocarbon surfactants. Experimental data is fitted to extract characteristic parameters, and a gradient-boosted regressor model is used for feature selection. The results show a good correlation between the machine learning models and experimental data.