Acting as an agent, the generator requires a pre-training stage to learn the essential underlying chemistry of MOFs. This knowledge includes which combinations of topology, metal clusters, and organic linkers can be constructed for MOFs and how to generate organic linkers of MOFs. The generator architecture is based on the Transformer architecture, which consists of an encoder and a decoder, as shown in comprising six connection points, whereas the tbo, a topology of HKUST-1, necessitates two types of vertices comprising 3 and 4 connection points. To address it, the encoder of the generator receives a metal cluster along with the number of connection points of an organic linker as inputs, and the decoder identifies the suitable topologies based on their connection points. To ensure sufficient diversity of organic linkers effectively, a scaffold-based approach was adopted for organic linkers, which was previously demonstrated as a successful methodology for generating diverse molecules with deep generative models . The decoder of the generator selects topologies and metal clusters sequentially and creates the SELFIES of organic linkers by retaining a batch of scaffolds employed as inputs for the encoder. The details of building scaffolds are explained in the Methods section.