CHARGE (Charged Hyperspherical Autoencoder with Repulsive Gradient Encoding) is a novel autoencoder designed with a unique latent space that is hyperspherical and of n dimensions. The model leverages a specialized loss function based on Coulomb's Law or the Inverse Square Law, promoting a more effective and efficient latent space. This approach enables CHARGE to perform better on generative AI tasks compared to traditional Variational Autoencoders (VAEs), as it is deterministic rather than probabilistic. The results show that CHARGE provides superior performance in generating realistic data and encoding meaningful latent representations, making it a promising candidate for various generative tasks in AI.