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This is a continued deep-dive into the latent space generated by the Orb model prior to it's MLFF tasks. I have been attempting to train a model on Tc prediction using this latent space as a feature v
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Visualizing the counts of materials in the training and evaluation dataset by their Tc. First bin is non-superconductors, the rest are ranges of 20 K increments.
Using the 256 dimensional latent space output from the Orb model, we visualize the 3DSC(MP) dataset using t-SNE and UMAP. The UMAP projection has been given the target for learning a manifold that keeps similar Tc materials close together.
Plot produced by taking the features generated by Orb (256 dim output) and visualizing different dimensionality reduction methods on them and coloring the point by Tc from the 3DSC database.