Fig. 4: Visualization of 2D t-SNE embedding of CNN features. | npj 2D Materials and Applications

Fig. 4: Visualization of 2D t-SNE embedding of CNN features.

From: Efficient water desalination with graphene nanopores obtained using artificial intelligence

Fig. 4

a 2D t-SNE embedding of features extracted from water flux prediction CNN model, where each point is colored by its predicted water flux. b 2D t-SNE embedding of features extracted from ion rejection rate prediction CNN model, where each point is colored by its predicted ion rejection. Each axis represents a dimension of the t-SNE embedding. Dot and X-marks represent graphene nanopores from DRL and the training dataset, respectively. Several graphene nanopores from the training dataset are shown in black and DRL-created membranes are shown in blue.

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