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After reading the MatterSim paper, the authors proposed the idea of using the MLFF's latent space as a direct property prediction feature set. Earlier, and I had been thinking about using a VAE (or s
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By increasing the level of certainty, requiring a higher probability for superconductivity, it's possible to get better results. However, this kind of tuning usually comes at a cost and other materials will suffer poorer results.
Seeing how the Orb latent space classifier model predicts materials with Tc greater than 90 K, when the model was only trained on materials with Tc less than 90 K.
These are the predictions made by a model that was trained on materials with max Tc of 90 K. Here we show how well it could predict a material (Ba2Ca1Cu2Hg1O6.24-MP-mp-6879-synth_doped) with a Tc greater than this range.
Cutting the dataset to train a model only on samples below 100 K, we test the model's ability to predict on materials with true Tc greater than 100 and the results are not good.