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. molecular graphs. These initial geometries are then refined by a NN to produce machine-learned representations that approximate the quality of high-accuracy DFT geometries. This approach enables the integration of accurate 3D structural information into downstream property prediction models without the computational cost of DFT calculations. Benchmark studies in the publication highlighted Uni-Mol+'s superior performance across a range of property prediction tasks, showcasing its robustness and computational efficiency. An interesting avenue for further work could be to explore how a similar geometry-refinement workflow might be adapted for predicting reaction barriers.