首页|Predicting the superconducting transition temperature of high-Temperature layered superconductors via machine learning
Predicting the superconducting transition temperature of high-Temperature layered superconductors via machine learning
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NSTL
Elsevier
High-temperature superconductors' critical temperature has been demonstrated to be correlated with the interlayer Coulombic coupling. In order to improve prediction accuracy and stabilities from simple algebraic expressions, we propose the Gaussian process regression model for predictions of critical temperature of high temperature superconductors from structural and electronic parameters. The model could be applied to a wide variety of superconductor families, including cuprate, ruthenate, rutheno-cuprate, iron-pnictide, ironchalcogenide, organic, and intercalated group-V-metal nitride-halides. It can be employed as an efficient and low-cost technique for predictions of critical temperature.
High-temperature superconductorSuperconductivityCritical temperatureGaussian process regressionMachine learningT-CELASTIC-MODULUSCOMPOSITES
NSTL
Elsevier
