ci7b00512_si_001.pdf (1.67 MB)
Predicting the Surface Tension of Liquids: Comparison of Four Modeling Approaches and Application to Cosmetic Oils
journal contribution
posted on 2017-11-01, 00:00 authored by Valentin Goussard, François Duprat, Vincent Gerbaud, Jean-Luc Ploix, Gérard Dreyfus, Véronique Nardello-Rataj, Jean-Marie AubryThe efficiency of
four modeling approaches, namely, group contributions,
corresponding-states principle, σ-moment-based neural networks,
and graph machines, are compared for the estimation of the surface
tension (ST) of 269 pure liquid compounds at 25 °C from their
molecular structure. This study focuses on liquids containing only
carbon, oxygen, hydrogen, or silicon atoms since our purpose is to
predict the surface tension of cosmetic oils. Neural network estimations
are performed from σ-moment descriptors as defined in the COSMO-RS
model, while methods based on group contributions, corresponding-states
principle, and graph machines use 2D molecular information (SMILES
codes). The graph machine approach provides the best results, estimating
the surface tensions of 23 cosmetic oils, such as hemisqualane, isopropyl
myristate, or decamethylcyclopentasiloxane (D5), with accuracy better
than 1 mN·m–1. A demonstration of the graph
machine model using the recent Docker technology is available for
download in the Supporting Information.