Abstract
This review examines popularity-aware discovery across ionizable lipids and atomically dispersed catalysts through a mechanism-to-decision framework. It asks how ranking systems should preserve underexplored molecular configurations. Evidence is organized around representation, validation, uncertainty, and operational control, with no invented experiments or unreported quantitative results. Particular attention is given to data-rich chemistries crowding out mechanistically promising alternatives. The synthesis shows that credible translation depends on explicit system boundaries, source-level traceability, failure-aware evaluation, and revalidation triggers. These principles provide a disciplined basis for nanomaterial and delivery-system discovery while keeping component promise distinct from system readiness.
References
Debe, M. K. (2012). Electrocatalyst approaches and challenges for automotive fuel cells. Nature, 486, 43-51. https://doi.org/10.1038/nature11115
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
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
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). https://doi.org/10.1145/2939672.2939778
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.
Wu, Z., Wang, T., Wang, S., Liu, N., & Zhang, Y. (2026). See further, think deeper: Advancing VLM's reasoning ability with low-level visual cues and reflection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 18870-18880).
Xia, F., Li, B., An, B., Zachman, M. J., Xie, X., Liu, Y., Xu, S., Saha, S., Wu, Q., Gao, S., Abdul Razak, I. B., Brown, D. E., Ramani, V., Wang, R., Marks, T. J., Shao, Y., & Cheng, Y. (2024). Cooperative atomically dispersed Fe-N4 and Sn-Nx moieties for durable and more active oxygen electroreduction in fuel cells. Journal of the American Chemical Society, 146(49), 33569-33578. https://doi.org/10.1021/jacs.4c11121
Yin, J., Zhu, B., Huo, P., Liu, R., Chen, H., Wang, S., Pan, S., & Zhang, C. (2026). Echoes in filter bubble: Diagnosing and curing popularity bias in generative recommenders. arXiv preprint arXiv:2605.16825. https://doi.org/10.48550/arXiv.2605.16825
Zhao, Y., Qu, Z., Paloncyova, M., Wang, Z., Weng, B., Zhao, Y., Liu, L., Song, D., Wich, D., Otyepka, M., & Xu, Q. (2025). Spatial conformation of ionizable lipids regulates endosomal membrane disruption. Journal of the American Chemical Society, 147(42), 38265-38274. https://doi.org/10.1021/jacs.5c10908