Augmenting the data with all possible resonance structures for each species provides an implicit way to learn resonance invariance, which has been shown to improve performance on tasks such as barrier height prediction for radical reactions. This approach involves generating a set of representative non-equivalent resonance structures for each molecule and assigning them the same target value. While several automated methods for chemical resonance generation exist, each has its limitations. For instance, RDKit 27 has a native resonance generation function, Chem.ResonanceMolSupplier, but it is not designed for radicals, which comprise a large part of our dataset.