Smart Membrane Design: Harnessing Machine Learning to Uncover Self-Assembly Dynamics for Advanced Water Purification

Abstract This research aims to deepen the understanding of membrane formation by employing machine learning to analyze how water flow influences the self-assembly of nanomaterials during fabrication. Focusing on surface nanofiltration (SNF) and hydroxyapatite (HAP) particles, the study utilizes Stokes’ law to model drag forces under varying conditions. The primary goal is to leverage data-driven insights to optimize membrane architecture, enabling more stable and efficient filtration systems for water purification. The study integrates classical fluid dynamics with modern machine learning techniques to explore membrane formation. The drag force acting on SNF and HAP particles was modeled using Stokes’ law, taking into account parameters such as particle radius, flow velocity, and resistance forces. Experimental data was used to train machine learning models—specifically, Random Forest and Gradient Descent algorithms—to predict drag force under diverse conditions. The models were designed to capture complex, nonlinear interactions between the influencing variables, enabling prediction and optimization of membrane self-assembly dynamics. The analysis revealed an inverse relationship between bead radius and drag force, where smaller particles experienced greater resistance—an essential factor in controlling membrane nanostructure. Conversely, a positive correlation was observed between flow velocity and drag force, indicating that higher velocities increase resistance, thus influencing particle deposition rates. The Random Forest model accurately predicted drag force with over 99% accuracy, significantly outperforming the Gradient Descent model. This high level of precision demonstrates the model's robustness in capturing complex interactions critical for optimizing membrane fabrication. These findings underscore the importance of balancing size and flow parameters to achieve structurally stable, high-efficiency membranes. This work pioneers a hybrid approach that fuses machine learning with fluid mechanics to unveil the hidden dynamics of membrane self- assembly. By creating a predictive framework grounded in physics and empowered by data, the study provides an innovative pathway for engineering high-efficiency membranes. This approach not only advances theoretical understanding but also introduces scalable solutions for real-world water purification challenges.