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Figure 9. (A) Flow chart of the DFT-GAN program structure: training and evaluation phases; (B) Discriminator and generator losses in DFT-GAN, with each iteration comprising 2,000 epochs; (C) The distribution maps of Ru and Rh atoms on the initial surface (iter = 0) and GAN-generated surfaces (iter = 1-5); (D) The TOF of NH₃ formation on Rh-Ru alloy surface, with TOF values from different DFT-GAN iterations (iter = 1-5) encoded in distinct colors; (E) Box plot and violin plot of TOF values for iter = 0-5. Left-side points on violin represent raw TOF values for each iteration. Copyright 2022, Springer Nature, Reproduced with permission^[126]; (F) Application potential of DL techniques in image-based catalyst screening, with recognizable image types including chemical images, morphological images, and catalytic images; (G) The workflow diagram of ML and DL for discovering HER electrocatalysts. Copyright 2021, American Chemical Society, Reproduced with permission^[127]; (H) Flow chart of the VAE network in AGoRaS: decompression of chemical database information into a high-dimensional latent space; (I) The flow chart of data collection, training, and validation steps employed by AGoRaS for generating synthetic data; (J) t-SNE visualizations of training and generated datasets. Upper panel: a sample of 7,000 equations from the training dataset, alongside 7,000 randomly selected equations from the generated dataset. Lower panel representation of 70,000 equations extracted from the generated dataset. Copyright 2022, Springer Nature, Reproduced with permission^[128]. DFT: Density functional theory; GAN: generative adversarial network; TOF: turnover frequency; DL: deep learning; ML: machine learning; HER: hydrogen evolution reaction; VAE: variational autoencoders; t-SNE: t-distributed stochastic neighbor embedding.