Machine Learning-Driven Development of Membrane Materials for Optimized Lithium Recovery Performance
Membrane separation technology, offering high separation efficiency, low energy consumption, and operational flexibility, is promising for lithium recovery. However, selective lithium extraction from complex matrices such as salt lake brines and battery leachates remains challenging. Traditional membrane development relies on empirical trial-and-error, suffering from low efficiency and the permeability-selectivity trade-off. This review systematically delineates machine learning (ML)-based frameworks for membrane material development, including high-throughput rational screening, inverse design of synthesis protocols, and high-fidelity performance prediction. We elucidate how advanced ML algorithms decipher structure-activity relationships at the molecular level, enabling breakthroughs in performance ceilings and guiding bottom-up fabrication of next-generation membranes. Critical challenges are assessed: scarcity of high-quality standardized datasets, limited model interpretability, and poor generalizability to industrial scales. Future directions emphasize physics-informed hybrid models, open-source global databases, and full-process system optimization to bridge laboratory innovation and industrial deployment.