• • ML-driven rational screening and inverse design can overcome the permeability-selectivity trade-off, achieving simultaneous enhancement of Li+/Mg2+ selectivity and permeance, as demonstrated in recent studies (e.g., polyamide membranes with exceptional solute-solute selectivity and permeance).
• • High-fidelity ML models predict membrane performance under diverse conditions, reducing experimental iterations by up to 70% and accelerating material development cycles from years to months.
• • Integration of ML with molecular simulations elucidates governing factors (e.g., polymer chemistry, pore size) for selective ion transport, enabling targeted design of membranes for lithium recovery from hypersaline brines.
• • Standardized open-source databases and physics-informed hybrid models are critical to improve model generalizability, with current lab-scale models showing limited accuracy (R² < 0.8) when extrapolated to industrial feed compositions.