With advances in machine learning predictions and high-throughput first-principles calculations, the pace and accuracy of computational materials science predictions has skyrocketed. The growth in the number of known stable, synthesizable materials, however, has not kept pace. Discovering new materials experimentally is challenging and time-consuming, and existing computational methods for materials generation are either inaccurate or too costly. To address this challenge of periodic materials generation, Xie, et al. developed a crystal diffusion variational autoencoder (CDVAE) that is able to generate stable materials that exist in the low-dimensional subspace of all possible periodic arrangements of atoms. Here, we validate over 3,000 CDVAE-generated structures with high-throughput DFT calculations and find that CDVAE greatly outperforms existing machine learning generative models by numerous metrics. Overall, the quality of the CDVAE-generated structures is very high, meaning that the model can serve as a means to rapidly expand the known materials genome.