This section will explore how ML can be used to solve chemical problems. Firstly, representations of molecules will be discussed, along with the concept of equivariance and its importance in handling Cartesian coordinates directly. Following this, a concise overview of key ML models prominent in the TS geometry prediction literature will be provided, as well as the key datasets used for training these models. Finally, metrics used to evaluate the performance of the models and pre-training, a technique for improving model performance, will be discussed.