Initial tests using only reactant and product geometries showed that DimeNet++ performed significantly worse than Chemprop, with a mean absolute error (MAE) of 6.2 kcal mol -1 compared to Chemprop's 2.9 kcal mol -1 . For reference, 1.0 kcal mol -1 is often regarded as the limit of chemical accuracy for QM calculations. improves activation energy barrier predictions by learning corrections to low-accuracy input geometries and energy barriers calculated using semi-empirical methods. Specifically, it uses the reactant, TS, and product geometries from the RGD1 dataset as inputs to predict corrections to the energy barrier while implicitly learning adjustments to the molecular geometries. Like Uni-Mol+, the Δ 2 approach avoids the direct use of computationally expensive optimised geometries, relying instead on machine-learned representations to achieve accurate predictions.