Lifecycle Reliability and Translation for Lithium-Sulfur Separator Engineering: Validity Domains and Revalidation Triggers

Abstract

Lithium-sulfur separator decisions span material selection, formation, cycling, degradation, maintenance, reuse, and retirement, yet AI models are often validated on a narrow segment of that lifecycle. This review defines lifecycle-specific validity domains for predictive models built from electrochemical sequences, impedance, thermal data, imaging, and manufacturing records. It evaluates how attention models, graph learning, and probabilistic prognostics can support early warning and remaining-life estimates while respecting changes in chemistry, protocol, and sensor availability. Revalidation is triggered by new material batches, altered cell design, distribution shift, calibration loss, unexplained residuals, or a decision context with different error costs. The framework records interventions because derating or replacement changes the future failure data and can bias learning. It also links predictions to explicit inspection, warranty, reuse, or retirement actions rather than treating accuracy as the endpoint. Based solely on the cited evidence, the article claims no new lifetime performance. Its contribution is a lifecycle AI assurance scheme that keeps reliability claims conditional, traceable, and renewable as the separator and its operating environment evolve.