The lack of ML models for discovering stable spintronic material structures motivated us to propose a new framework. In the computational material science field, structure prediction is one of the key problems. Recent studies have shown that deep learning based generative models can be used to generate new stable crystal structures. In materials informatics, two types of generative models can be trained to generate crystal structures: Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). A VAE model contains an encoder and a decoder: the encoder learns to represent materials with latent vectors and the decoder reconstructs the materials via latent vectors. After training, the decoder can be used to sample new materials. iMatGen is the first work that uses VAEs to generate metastable V x O y materials. Later works use VAEs generate different types of materials with variations of VAEs' architectures. Like iMatGen, they have difficulty in generating high-symmetry materials because the VAEs do not have symmetry information in their training. On the other hand, A GAN also has two parts: a generator and a discriminator. The generator takes random noise as input to generate fake samples and the discriminator tells fake samples from real ones. CubicGAN and PGCGM are two typical crystal generative models using GANs. Both are provided with the space group in the training and physical losses are added in PGCGM to improve the performance. With symmetry information and physical losses, both can generate stable materials.