A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries. (15th September 2021) - Wan, Shuaibin; Liang, Xiongwei; Jiang, Haoran; Sun, Jing; Djilali, Ned; Zhao, Tianshou
Highlights: A data-driven approach is developed for the design of flow battery electrodes. A custom-made dataset containing 2275 fibrous structures is generated. A structure–property map of fibrous structures is established. Fiber diameters of around 5 μm with aligned arrangements are preferable. Abstract: The design of porous electrodes with large specific surface area and high hydraulic permeability is a longstanding target for the development of redox flow batteries (RFBs), but traditional trial-and-error strategies are hindered by the heavy cost of collecting large amounts of data and the limitation of human intuition when multiple trade-offs are at play. In this work, a novel framework coupling machine learning and genetic algorithm is developed to identify the optimal electrode structures for RFBs. A custom-made dataset containing 2275 fibrous structures is first generated by adopting a combination of stochastic reconstruction method, morphological algorithm, and lattice Boltzmann method. Based on the dataset, our best machine learning models allow to achieve test errors of 1.91% and 11.48% for predicting specific surface area and hydraulic permeability, respectively. Combined with well-trained prediction models, the genetic algorithm is developed to screen more than 700 promising candidates with up to 80% larger specific surface area and up to 50% higher hydraulic permeability than the commercial graphite felt electrodes. Results show that the fiber diameter and electrode porosity of these promising candidates exhibit a triangle-like joint distribution, with a preference for fiber diameters of around 5 μm with aligned arrangements.