The microstructure of porous electrodes determines multiple performance-defining properties, such as the available reactive surface area, mass transfer rates, hydraulic resistance, and electronic conductivity. Thus, optimizing the electrode architecture is a powerful approach to enhance the performance and cost-competitiveness of electrochemical technologies. To this goal, the deployment of computational modeling has improved the fundamental understanding of reactive transport in porous electrodes; however, to date, it has mostly been used to simulate existing sets of materials. To expand our current arsenal of materials, we need to build computational frameworks that are predictive and can explore a large geometrical design space while being physically robust. Here, we present a novel approach for the optimization of porous electrode microstructure from the bottom-up that couples a genetic algorithm with a chemistry-agnostic electrochemical pore network model. In this first demonstration, we focus on optimizing redox flow battery electrodes. The genetic algorithm manipulates the pore and throat size distributions of an artificially generated microstructure with fixed pore positions by selecting the best-performing networks, based on the hydraulic and electrochemical performance computed by the model. For the studied VO2+/VO2+ electrolyte, we find an increase in the fitness of 75% compared to the initial configuration. The algorithm improves the fluid distribution by the formation of a bimodal pore size distribution containing preferential longitudinal flow pathways, resulting in a decrease of 73% for the required pumping power. Furthermore, the optimization yielded an 47% increase in surface area resulting in an electrochemical performance improvement of 42%. Our results show the potential of using genetic algorithms combined with pore network models to optimize porous electrode microstructures for a wide range of electrolyte composition and operation conditions.
Appendix - Starting from the bottom: Coupling a genetic algorithm and pore network model for porous electrode design