MatterGen implements a generative model that synthesizes novel inorganic crystal structures across the entire periodic table. The model can be fine-tuned to steer generation toward specific property targets such as stability, electronic properties, or mechanical characteristics. It provides pre-trained checkpoints, training pipelines, and evaluation tools for researchers working on materials discovery and computational chemistry.