Bottom-up design of porous electrodes by combining a genetic algorithm and a pore network model

The microstructure of porous electrodes determines multiple performance-defining properties, such as the available reactive surface area, mass transfer rates, and hydraulic resistance. Thus, optimizing electrode architecture is a powerful approach to enhance performance cost-competitiveness electrochemical technologies. To expand our current arsenal materials, we need build predictive frameworks that can screen large geometrical design space while being physically representative. Here, present novel for optimization microstructures from bottom-up couples genetic algorithm with previously validated pore network model. In this first demonstration, focus on redox flow battery electrodes. manipulates throat size distributions an artificially generated fixed positions by selecting best-performing networks, based computed For studied VO2+/VO2+ electrolyte, find increase in fitness 75 % compared initial configuration minimizing pumping power maximizing system. generates structures improved fluid distribution through formation bimodal containing preferential longitudinal pathways, resulting decrease 73 required power. Furthermore, yielded 47 area improvement 42 %. Our results show potential using algorithms combined models optimize wide range electrolyte composition operation conditions.