Due to being a key molecular property for various fields of chemistry and the availability of openly accessible datasets , ML-based solubility predictions have been increasingly employed . In contrast, ML-based predictions of redox potentials for electrolyte materials have not been sufficiently well investigated. Although some recent studies demonstrated promising results for the applicability of ML-based screening of redox-active materials , accurate and comprehensive ML models are still needed to accelerate the screening of candidate AORFB electrolyte materials from diverse chemical spaces.