Firstly, each MOF in the QMOF database (total of 20,000 structures) is featurized, and a forward pass of the model is used. Then, each forward pass gives us the contextual embeddings and the predicted target. The contextual embeddings (a vector with 768 dimensions for each MOF) are reduced to 2 dimensions using UMAP. This process is then repeated for each target.