Redox flow batteries (RFBs) hold promise as an electrochemical intermediary between intermittent renewable power generation and the grid.¹ Within the RFB architecture, porous electrodes are an integral component, contributing to a myriad functions, such as providing surface sites for redox reactions, dispersing electrolyte throughout the void matrix, and shuttling electrons through its conductive network.² Carbonaceous materials, such as cloths, papers, and felts, are commonly used in state-of-the-art RFB embodiments, despite their tailored morphologies in polymer electrolyte fuel cells. Consequently, these media engender suboptimal performance in RFBs, necessitating advancement of electrode structures specifically designed for flow battery operation. While there have been previous systematic studies of RFB electrode structure/function relationships,^3,4 most have been limited by manufacturing constraints, restricting the explorable design space. However, recent computational efforts have enabled broad sweeping property analyses,⁵ leveraging both data science and machine learning algorithms to identify key parameter values. These modeling and statistical pursuits, combined with advanced cell diagnostics, provide a platform for high-throughput material screening in the furtherance of electrode engineering. In this presentation, we highlight some of the computational tools we have developed for simulated analyses of electrode functionalities in RFBs. We then present experimental tools for in situ and operando electrode benchmarking, which can also be simulated with the aforementioned computation toolkit. Finally, we conclude by demonstrating how this combined platform can be used across a wide variety of electrode domains, revealing performance comparisons and micron-scale designs to direct manufacturing avenues. Acknowledgments This work was funded by the Joint Center for Energy Storage Research, an Energy Innovation Hub of the U.S. Department of Energy, Office of Science, Basic Energy (De-AC02-06CH11357). K.M.T. recognizes additional support from the U.S. NSF Graduate Research Fellowship (1122374). References A. Z. Weber et al., J. Appl. Electrochem, 41, 1137 (2011). K. J. Kim et al., J. Mater. Chem. A, 3, 16913–16933 (2015). A. Forner-Cuenca, E. E. Penn, A. M. Oliveira, and F. R. Brushett, J. Electrochem. Soc., 166, A2230–A2241 (2019). K. M. Tenny, A. Forner-Cuenca, Y.-M. Chiang, and F. R. Brushett, J. Electrochem. En. Conv. Stor, 17 (2020). Z. Cheng et al., Applied Energy, 279, 115530 (2020).