Cross-Scale Modelling and Validation for Tapered Graphene Desalination: Traceable Translation under Uncertainty

Abstract

Tapered graphene desalination joins atomic-scale interactions and pore geometry to membrane-scale flux, rejection, fabrication variation, and module operation. This review evaluates how AI can bridge those levels without masking the uncertainty introduced at each transition. Physics-informed surrogates represent molecular and continuum constraints, hierarchical models propagate parameter and measurement uncertainty, and domain-shift tests assess whether simulations remain relevant to fabricated channels and realistic feedwater. Validation is staged: local transport behavior, geometry sensitivity, membrane aggregation, and decision utility must each be supported before scale-up. The synthesis records simulation settings, material batches, preprocessing, checkpoints, and comparator choices in a traceable evidence graph. Agreement in average trends is insufficient when local failure modes or calibration differ; unsupported regimes trigger active measurement or abstention. The article presents no new simulation or desalination trial. Its contribution is a cross-scale AI framework that makes translation auditable, preserves uncertainty from nanoscale mechanism to operational decision, and identifies the evidence needed to justify each step beyond the laboratory.