A comprehensive overview of microemulsions innovations through artificial neural network approaches

Microemulsions are multifunctional complex colloidal dispersed systems with widely utilized applications in drug delivery systems and chemical engineering. The interwoven relationship within their compositional variables, like surfactants, oil-to-water ratios, and co-surfactant type, leads to highly nonlinear phase behaviors that are difficult to analyze using traditional empirical or mechanistic models. This narrative review mainly focuses on the emerging role of artificial neural networks (ANNs) in optimizing microemulsion systems. Initially, the current study contextualizes the physicochemical factors of microemulsions and identifies their computational bottlenecks in formulation and phase behavior predictions. The review then analyses the relevant neural network structures, including feed forward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), for assessing their applicability to high-dimensional regression and classification and, furthermore, to reduce experimental load in microemulsion research. One of the advancements of using ANN is that it can identify the ideal concentration of excipients for the desirable properties of emulsion. Case studies are addressed wherein neural networks have been tutored on experimental and simulated datasets to estimate the droplet size distribution, construct pseudo-ternary phase diagrams, and identify optimal formulation properties. In addition to that, emphasis is applied to model structural design, feature selection strategies, and model validation techniques. The study also considers the current obstacles, such as paucity of data availability, over-fitting, and the integration of expertise knowledge in the learning models. Looking forward to the next context, this review illustrates that artificial neural network-based approaches provide a scalable and adaptable computational framework for boosting innovation in microemulsion science.