Next we apply this approach to selecting new, novel synthesis targets found in computational datasets. GNOME leveraged a deep learning framework to predict crystal stability. The approach resulted in the "discovery" of 2.2 million crystal structures, 380,000 of which are predicted to be thermodynamically stable . A selection of these GNOME materials are available through the Materials Project. To assess the relative novelty of these materials are, we apply our approach to a small subset of the dataset and look at compounds containing lithium and at least one other element. To serve as an existing corpus, we pulled all experimentally verified lithium containing structures from the Materials Project, totaling 1834 materials. The mutual information cutoff and profile were computed over these to establish knowledge on the existing density data. Next the 44 lithium containing compounds in the contributed GNOME dataset were individually assessed against the existing corpus. This was performed so as isolate each GNOME materials density against the existing corpus and avoid the influence from other GNOME materials.