Experiments with diffusion models to generate crystal structures, moving from noisy representations to concrete atomic arrangements. They describe learning how these models can learn structure without strict physical rules, and compare approaches that rely on fixed constraints to ones that let the model discover valid layouts. The author notes limitations in existing crystal generators, such as only producing tiny unit cells and struggling with complex, multi-atom systems like NdFeB. To address this, they explore modeling larger supercells with hundreds of atoms to improve detail and tolerance to errors, potentially revealing new properties through dopants. They keep a running experiment log in an AI notebook and plan to explore conditioning methods and the difference between flow and diffusion approaches in future work.