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Visualizing a selection of the most important latent features and how the relate to transition temperature.
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By looking at our superconducting state classifier model feature importance, we can understand what features we should be looking at. From there, we can start to study how these features change across
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Top 20 features selected for the regression analysis of Tc, where (a) is without and (b) is with MEGNet element embeddings, along with the realized total loss reduction (i.e., the relevance score).
We find a similar idea that high-Tc materials form clusters and are generally a sweet spot within the range of a feature's values.
Here we showcase a few key latent features and their relationships to each other, points colored by their Tc. We animate from 0 K to ~130 K.