@article{Seddon:2022:10.1016/j.jcis.2022.06.034,
author = {Seddon, D and Müller, EA and Cabral, JT},
doi = {10.1016/j.jcis.2022.06.034},
journal = {Journal of Colloid and Interface Science},
pages = {328--339},
title = {Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution},
url = {http://dx.doi.org/10.1016/j.jcis.2022.06.034},
volume = {625},
year = {2022}
}

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TY  - JOUR
AB  - 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 °C is fitted to the Szyszkowski equation to extract three characteristic parameters (Γmax,KL and critical micelle concentration (CMC)) which are correlated to a series of 2D and 3D molecular descriptors. Key (∼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 R2= 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.
AU  - Seddon,D
AU  - Müller,EA
AU  - Cabral,JT
DO  - 10.1016/j.jcis.2022.06.034
EP  - 339
PY  - 2022///
SN  - 0021-9797
SP  - 328
TI  - Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution
T2  - Journal of Colloid and Interface Science
UR  - http://dx.doi.org/10.1016/j.jcis.2022.06.034
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35717847
UR  - http://hdl.handle.net/10044/1/98039
VL  - 625
ER  - 

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