Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks.

ABSTRACT: The hydrophobicity of oils is a key parameter to design surfactant/oil/water (SOW) macro-, micro-, or nano-dispersed systems with the desired features. This essential physicochemical characteristic is quantitatively expressed by the equivalent alkane carbon number (EACN) whose experimental determination is tedious since it requires knowledge of the phase behavior of the SOW systems at different temperatures and for different surfactant concentrations. In this work, two mathematical models are proposed for the rapid prediction of the EACN of oils. They have been designed using artificial intelligence (machine-learning) methods, namely, neural networks (NN) and graph machines (GM). While the GM model is implemented from the SMILES codes of a 111-molecule training set of known EACN values, th

SUBMITTER: Delforce L 

PROVIDER: S-EPMC9631404 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature