Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
Promising separator behavior in a controlled lithium-sulfur cell does not automatically transfer to different chemistries, formats, manufacturing lines, or duty cycles. This review specifies the evidence boundaries that govern AI-supported scale-up. Material composition, pore architecture, polysulfide interaction, cell assembly, cycling protocol, sensing, and decision use are represented as distinct but linked domains. Physics-informed learning and domain adaptation may bridge selected gaps, while out-of-distribution detection and calibrated uncertainty identify when a bridge is unsupported. The synthesis requires each model to declare its validation envelope and predefine triggers for recalibration, shadow testing, new cell studies, or production re-qualification. It emphasizes that an unchanged prediction interface can hide substantial changes in data generation or error consequences. Provenance records connect separator batches and test conditions to model versions and release decisions. This article does not generate a new scale-up result. Its contribution is a traceable AI translation framework that separates defensible transfer from extrapolation and turns revalidation from an informal response into a planned engineering control.
References
Debe, M. K. (2012). Electrocatalyst approaches and challenges for automotive fuel cells. Nature, 486, 43-51. https://doi.org/10.1038/nature11115
Hollnagel, E., Woods, D. D., & Leveson, N. (Eds.). (2006). Resilience engineering: Concepts and precepts. Ashgate.
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International Conference on Learning Representations.
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
Norskov, J. K., Rossmeisl, J., Logadottir, A., Lindqvist, L., Kitchin, J. R., Bligaard, T., & Jonsson, H. (2004). Origin of the overpotential for oxygen reduction at a fuel-cell cathode. The Journal of Physical Chemistry B, 108(46), 17886-17892. https://doi.org/10.1021/jp047349j
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
Tao, J., Lyu, R., & Cao, X. (2026). A scalable data governance architecture for privacy-aware intelligent learning systems in lifelong. Future-Adaptive Intelligence and Lifelong Systems, 1(1).
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Wang, Z., Li, W., Wang, T., Pang, M., Kong, Z., An, J., Li, Z., Ye, J., & Xia, G. (2025). Nanoporous ZnGa2O4-modified separator as a multifunctional polysulphide barrier for advanced lithium-sulfur batteries. Electrochemistry Communications, 178, 107990. https://doi.org/10.1016/j.elecom.2025.107990
Wang, Z., Zhang, K., Wang, Y., Zhang, C., Liang, Q., Yu, P., Feng, Y., Liu, W., Wang, Y., Bao, Y., & Yang, Y. (2022). SongDriver: Real-time music accompaniment generation without logical latency nor exposure bias. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 1057-1067). ACM. https://doi.org/10.1145/3503161.3548368