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<p>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 surfactant component, which are then aggregated using strategies such as concatenation, arithmetic mean, and harmonic mean. We find that pairing the arithmetic mean strategy with ANN yields the best performance, effectively capturing complex molecular interactions and enabling dual predictive capabilities: (1) precise interpolation of CMC values at untested mole fractions within known mixtures, and (2) accurate prediction of complete CMC–composition profiles for entirely novel surfactant combinations. SHAP-based interpretability analysis highlights that features such as hydrophobic surface area, electronic topological descriptors, and headgroup basicity drive model predictions, aligning with core principles of surfactant chemistry and reinforcing the mechanistic validity of our model. Overall, this framework accelerates data-driven surfactant design by reducing experimental burden and enabling rapid, rational optimization of formulations across pharmaceuticals, personal care, environmental remediation, and enhanced oil recovery.</p><p><b>Scientific contribution</b></p><p>This study presents a novel machine learning framework that, for the first time, predicts full critical micelle concentration (CMC)–composition profiles for binary surfactant mixtures, including untrained systems. By strategically combining the features of individual components of mixtures using arithmetic&#xa0;mean, our artificial neural network model deciphers nonlinear interactions between chemically distinct surfactants, enabling accurate and generalizable CMC predictions. Beyond performance gains, this framework facilitates rapid and systematic exploration of formulation space via inverse design and high-throughput screening, establishing a powerful foundation for the rational development of next-generation surfactants with applications in energy, environmental remediation, pharmaceuticals, and biomedical science.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月12日

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  • Aditya Choudhary,
  • Saaketh Desai,
  • Methun Kamruzzaman,
  • Alexander Landera,
  • Koushik Ghosh,
  • Kunal Poorey

摘要

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 surfactant component, which are then aggregated using strategies such as concatenation, arithmetic mean, and harmonic mean. We find that pairing the arithmetic mean strategy with ANN yields the best performance, effectively capturing complex molecular interactions and enabling dual predictive capabilities: (1) precise interpolation of CMC values at untested mole fractions within known mixtures, and (2) accurate prediction of complete CMC–composition profiles for entirely novel surfactant combinations. SHAP-based interpretability analysis highlights that features such as hydrophobic surface area, electronic topological descriptors, and headgroup basicity drive model predictions, aligning with core principles of surfactant chemistry and reinforcing the mechanistic validity of our model. Overall, this framework accelerates data-driven surfactant design by reducing experimental burden and enabling rapid, rational optimization of formulations across pharmaceuticals, personal care, environmental remediation, and enhanced oil recovery.

Scientific contribution

This study presents a novel machine learning framework that, for the first time, predicts full critical micelle concentration (CMC)–composition profiles for binary surfactant mixtures, including untrained systems. By strategically combining the features of individual components of mixtures using arithmetic mean, our artificial neural network model deciphers nonlinear interactions between chemically distinct surfactants, enabling accurate and generalizable CMC predictions. Beyond performance gains, this framework facilitates rapid and systematic exploration of formulation space via inverse design and high-throughput screening, establishing a powerful foundation for the rational development of next-generation surfactants with applications in energy, environmental remediation, pharmaceuticals, and biomedical science.

Graphical Abstract