A data-driven approach to interfacial polymerization exploiting machine learning for predicting thin-film composite membrane formation
Gergő Ignácz, Muhammad Irshad Baig, Karuppasamy Gopalsamy, Andres Villa, Suzana P. Nunes, Bernard Ghanem, Tejus Shastry, Sanat K. Kumar, György Székely · Materials Horizons · 2025
AFM and optical microscopy. This unprecedentedly large and open access dataset marks a considerable step toward data-driven thin-film membrane development. We trained five machine learning models on molecular structures and density functional theory calculations to study film formation parameters and their binary outcomes. The results indicate that film formation can be predicted directly from monomers, facilitating the potential of data-driven membrane development. Our work shifts the focus from performance prediction to the fundamental step of thin-film formation, offering a new perspective in data-driven membrane research.