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1,634 claims from 1,009 papers are on the record. 46 have been checked so far; the other 1,588 have no check with a result yet.

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Keyword: superconductivity ×Clear all

3 claims from 2 papers

1. Materials Science › Machine Learning in Materials Science

   Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials

   Schmidt, Hoffmann, Wang et al. · Advanced Materials · 2023

   The authors built a more balanced dataset to train crystal-graph neural networks on stability, then used them to search a billion candidate materials and find new stable compounds.

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   1. UncheckedCrystal-graph neural networks trained on the authors' new, more balanced dataset reach what the paper calls unprecedented generalisation accuracy.“Crystal‐graph neural networks trained with this dataset show unprecedented generalization accuracy.”
   2. Unchecked“In this way, the number of vertices of the global T = 0 K phase diagram is increased by 30% and find more than ≈150 000 compounds with a distance to the convex hull of stability of less than 50 meV atom −1 .”

2. Materials Science › Machine Learning in Materials Science

   Machine learning modeling of superconducting critical temperature

   Stanev, Oses, Kusne et al. · MPG.PuRe (Max Planck Society) · 2017

   Machine learning models were built on the SuperCon database to classify and predict superconducting critical temperatures, then used to search a crystal database for candidate new superconductors.

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   1. UncheckedA model using only chemical composition to sort superconductors by whether Tc is above or below 10 K reached about 92% out-of-sample accuracy.“It shows strong predictive power, with out-of-sample accuracy of about 92%.”