To showcase the capabilities of the developed generative AI model, we target several distinct chemical spaces. The first Ti-Zn-O system has been extensively explored and contains many known materials, such as TiO2 and ZnO. The second system is Li-P-S-Cl, representing a complex quaternary space that is sparsely populated, with a limited number of known materials despite its technological relevance in electrochemical energy storage. The challenge of multi-component chemical spaces, like ternary or quaternary systems, lies in their vast size. Even with a maximum stoichiometric coefficient of 6, for instance, the Li-P-S-Cl system has 2,400 possible combinations, calculated as 7 -1. Each element can have a stoichiometry ranging from 0 to 6 and excluding those where coefficients are zero. This vastness makes comprehensive exploration time-consuming and computationally demanding.