Membrane Formation by Phase Separation: Mechanisms, Simulation, and Machine-Learning-Assisted Modeling

Machine learning (ML), including deep learning, has created opportunities for analyzing nonequilibrium membrane-formation processes that remain difficult to interpret through experiments or conventional models alone. Although ML is already widely used for membrane property prediction, its role in elucidating membrane-formation mechanisms remains comparatively underdeveloped. This review offers an integrative, formation-centered synthesis of physical chemistry, multiscale simulation, and data-driven modeling in membrane science. We examine route-dependent phase-separation mechanisms across immersion-induced, evaporation-assisted, thermally induced, and chemically induced pathways─treating NIPS as a major but not exclusive archetype─and emphasize how thermodynamic trajectories and kinetic constraints jointly shape membrane morphology. We then discuss how molecular dynamics (MD) and dissipative particle dynamics (DPD) connect molecular interactions with mesoscale phase-separation dynamics, while highlighting parametrization challenges, scale-transfer assumptions, and computational trade-offs. In parallel, we assess how ML can support thermodynamic parameter estimation, miscibility classification, morphology prediction and reconstruction, surrogate dynamics, and process-oriented screening. By connecting phase-separation principles, multiscale simulation, and formation-specific data-driven modeling within a single comparative framework, this review aims to consolidate a more mechanistic and evidence-aware basis for membrane design: ML may support screening, prioritization, and surrogate-based exploration when data sets are standardized, descriptors are physically meaningful, and predictions are validated against independent experiments, rather than offering a turnkey route to rational design.