Reentrant superconductivity as an emergent behavior of Tc classifier

In this material (Cu1La2O4.066-MP-mp-1077929-synth_doped), we may have signs of reentrant superconductivity, where below a maximum Tc there are actually multiple bands of superconductivity.

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Reentrant superconductivity as an emergent behavior of Tc classifier

In this material (Cu1La2O4.066-MP-mp-1077929-synth_doped), we may have signs of reentrant superconductivity, where below a maximum Tc there are actually multiple bands of superconductivity.

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Orb latent space Tc classifier
Using what we learned when trying to use the MLFF's latent space for Tc prediction, there's a way we can simplify things for the prediction model and give it a better change of picking up on the signa
in•2d•updated 22h ago
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Machine learning modeling of superconducting critical temperature

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their Tc values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. https://www.nature.com/articles/s41524-018-0085-8

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