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Prediction of superconducting properties of materials based on machine learning models
Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials.
Prediction of properties of metal alloy materials based on machine learning
The experimental results show that the machine learning can predict the material properties accurately.
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