Programming Interfacial Polymerization: Machine Learning Unveils Quantitative Rational Design Rules for Microcapsules and Beyond

Abstract

ABSTRACT Interfacial polymerization (IP) serves as a versatile platform technology for designing polymeric membranes, yet its extension to applications such as microencapsulation (MIP) remains hindered by empirical methodologies, largely due to the absence of quantitative rational design principles. Unlike separation membranes, which prioritize nanostructural control, MIP emphasizes encapsulation efficiency (EE%), rendering conventional membrane‐derived theories and thermodynamic descriptors insufficient. In this work, we transcend these limitations by employing interpretable machine learning to program interfacial polymerization, thereby deciphering mechanism‐informed quantitative design rules. Our data‐driven platform integrates molecular thermodynamics, polymerization kinetics, and emulsion‐stabilized interfacial parameters to identify previously overlooked descriptors governing microcapsule formation. We establish a predictive chemical–process–structure–performance relationship and demonstrate programmable control over key performances, including EE% (30%–95%), particle size (100–400 µm), and shell thickness‐to‐radius ratios (0.005–1) for diverse payloads spanning hydrophobic, hydrophilic, and highly reactive compounds such as toluene diisocyanate and amines. This work not only resolves long‐standing challenges in understanding complex multiphase interactions in MIP but also establishes a new paradigm for the quantitative design of polymeric microcapsules, with broad implications for functional particles, catalytic microreactors, digital cells, and membranes.