However, direct comparison of the metrics from different publications remains challenging, as models were trained at different times, on varying hardware, and using distinct datasets with different train-test splits and chemistry. This underscores the need for a standardised benchmarking framework, where methods and architectures are evaluated on identical datasets using consistent training strategies to enable fair comparisons. Moreover, many models have been trained on small datasets, such as the isomerisation and Transition1x datasets, which only feature molecules with up to seven heavy atoms.