Generative adversarial networks (GANs) are another class of generative models containing two deep neural networks: a generator (𝐺) and a discriminator (𝐷). 𝐺 and 𝐷are 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 56 and video generation. In chemistry, their use has been more limited but includes inorganic material generation 58 and molecule generation. For more information on GANs, please refer to Refs. .