Generative adversarial networks (GANs) are another class of generative models containing two deep neural networks: a generator (𝐺) and a discriminator (𝐷). 𝐺 and 𝐷 are trained against each other as part of a minimax game, where 𝐺 aims to generate samples resembling the training data distribution to fool 𝐷, and 𝐷 aims to distinguish the "fake" (generated) data from the real data. GANs have been used in areas such as image-to-image translation, data generation 58 and video generation. In chemistry, their use has been more limited but includes inorganic material generation 60 and molecule generation. Like diffusion models, GANs are capable of capturing complex spatial information, which contributed to their success in generative tasks during the mid-to-late 2010s. This capability suggests that GANs could also be a viable approach for predicting TS geometries. Transformers are another type of NN architecture that transform an input sequence of data to an output sequence. A self-attention mechanism is used to handle long-range dependencies in sequential data, enabling effective modelling of the complex relationships present. Transformers have been used for a wide variety of different tasks, including natural language processing, 64 image recognition and speech recognition, and in chemistry for synthesis and retrosynthesis, 67 molecular property prediction and reaction prediction .