Pre-training is one example which leverages large amounts of data to learn something generally, rather than the model being trained to achieve a specific task. This approach is becoming more commonly used in many areas of chemistry, including molecular property prediction and in predicting TS geometries. Pre-training can be especially effective in chemistry because there are fundamental principles that are difficult to encode in a model from scratch. By completing self-supervised tasks on large bodies of chemical data, such as predicting masked atoms or the original positions of noised atoms, 116 the model can develop an understanding of common chemical structures and patterns of elements which do and do not occur. Additionally, for TS systems in particular the availability of datasets is limited, with most typically being small in size and lacking diversity in the chemistry covered (see Table ). Being able to take advantage of large non-TS datasets helps instil general chemical principles before learning the intricacies of TS structures. After pre-training, the model will be fine-tuned to the primary task which often results in superior performance. Unlike conventional convolutional neural networks, commonly used for tasks like image classification and speech recognition, 123,124 SchNet's cfconv layers are tailored to handle the continuous spatial distribution of atoms, capturing essential physical information that would otherwise be lost by approximating atomic positions on a grid. SchNet processes Cartesian coordinates to produce atomwise representations, which are refined through successive cfconv layers to account for the spatial relationships between atoms. This method accurately models atomic interactions and provides energy and force predictions that are invariant to molecular rotations and translations-a necessary feature since a molecule's physical properties remain unchanged under these transformations. SchNet's performance was benchmarked on the ISO17 dataset, consisting of 129 molecules with 5,000 conformational geometries, where it achieved state-of-the-art predictions at the time of publication in 2017.