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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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Watching how SHAP values change as Mo6Pb0.1Se8Yb0.9-MP-mp-1103921-synth_doped is heated from 0 K to 26 K. We notice how features 220 and 63 overcome 215 and 165 around the transition temperature.
Simulating the material heating up to 300 K, we watch as the probability of the superconducting state gradually and steadily decreases, which makes it hard to choose an exact Tc.
Looking at a material (B2Mg0.95Zr0.05-MP-mp-763-synth_doped) with a higher Tc, we see that the model is still able to make good classifications near the true critical temperature.
Comparing how feature 63 and corresponding SHAP values changes as a material increases in temperature