Predicting the critical micelle concentration of binary surfactant mixtures using machine learning.

ABSTRACT: Surfactant mixtures play a critical role in industries such as drug delivery, cosmetics, firefighting foams, and lubrication, serving as foundational components of the global economy. Their performance hinges on micelle formation, a self-assembly process governed by the critical micelle concentration (CMC), which enables key functions like solubilization, emulsification, and targeted molecular delivery. However, rapidly and accurately predicting the CMC of mixtures remains a significant challenge due to the chemical diversity and nonlinear interactions between surfactants. Here, we introduce an artificial neural network (ANN)-based machine learning framework to predict the CMC of binary surfactant mixtures. Our workflow leverages cheminformatics-derived molecular descriptors for each surfa

SUBMITTER: Choudhary A 

PROVIDER: S-EPMC12613689 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature