In a similar fashion, a Δ 2 approach introduced by Zhao et al. provides another example of how implicitly learned representations of geometries can improve predictions while reducing computational demand. Building on the Δ-ML framework proposed by Ramakrishnan et al., the Δ 2 approach 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.