The performance of Diffusion Models in the materials discovery space creates a lot of opportunity for future works. The method of Diffusion Model generation we used in our work is known as unconditional generation. Conditional generation is the method that was responsible for all of the text-to-image breakthroughs that underpin powerful tools such as DallE-2 and Imagen . It is possible to apply conditional generation to our work, as chemical formulato-crystal generation. This could work by taking any chemical formula from PCD such as Ca 3 AlB 2 [OH] 15 [H 2 O] 11 and transforming it into a natural language analog such as "three calcium atoms, one aluminum atom, two boron atoms, fifteen hydroxide (one oxygen atom, one hydrogen atom) molecules, eleven water (one oxygen atom, two hydrogen atoms) molecules." This, along with increasing the complexity of our Diffusion Models and adding chemical descriptors as additional layers to CrysTens will be our next step in this space.