Green Extraction of Rare Earth Elements Using Functionalized Polymer Sorbents: An AI-Driven Approach

Rare earth elements are critical to decarbonization, advanced electronics, and defense applications. Traditional hydrometallurgical recovery using liquid–liquid solvent extraction (SX) and classical ion exchange, however, requires the use of volatile organic diluents, high chemical consumption, and multiple-stage cascades, which produce significant amounts of saline and acidic effluents. Solvent-impregnated resins (SIRs), polymer inclusion membranes (PIMs), microbeads (PIBs), and cross-linked networks are environmentally friendly polymeric sorbents, which can greatly reduce the bleeding of the organic phase. This perspective follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and critically examines the literature on polymer chemistry and artificial intelligence (AI) from 2016 to 2026. We investigate gradient boosting architectures and deep neural networks that map topological fingerprints and molecular physicochemical descriptors to distribution coefficients, coordination thermodynamics, and sorption capacities (R2 = 0.81–0.93). Furthermore, we critically evaluate three major translational limitations: integrating transfer learning throughout the 4f lanthanide contraction, performance degradation across complex real-world leachates, and bridging small-molecule informatics with cross-linked macromolecular networks. Finally, Bayesian inverse design and autonomous experimentation are contextualized to guide the scalable development of next-generation, high-selectivity polymeric sorbents.