Machine learning-enabled performance prediction and optimization for iron–chromium redox flow batteries

Iron–chromium flow batteries (ICRFBs) are regarded as one of the most promising large-scale energy storage devices with broad application prospects in recent years. In this work, active learning is used to explore the most optimized cases considering the highest energy efficiency and capacity.