Another fundamental aspect of using ML in chemistry is the data itself. The effectiveness and generalisability of an ML model is tied to the accuracy and diversity of the training data, so it is essential that the datasets used for training are reliable and comprehensive. The key datasets in the TS geometry prediction literature will be discussed in Table . Isomerisation 103 • Reactant, product and TS geometries of 12,000 unimolecular isomerisation reactions, sampled from the larger GDB-17 molecular dataset. • Geometries were calculated using the growing string method at 𝜔B97X-D3/def2-TZVP 104-107 level of theory.