Abstract
Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers’ attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symmetry. By using doping methods and dopant levels, different types of new Fe-based superconductors are synthesized. The transition temperature is a key indicator to measure whether new superconductors are high temperature superconductors. However, the condition for measuring transition temperature are strict, and the measurement process is dangerous. There is a strong relationship between the lattice parameters and the transition temperature of Fe-based superconductors. To avoid the difficulties in measuring transition temperature, in this paper, we adopt a machine learning method to build a model based on the lattice parameters to predict the transition temperature of Fe-based superconductors. The model results are in accordance with available transition temperatures, showing 91.181% accuracy. Therefore, we can use the proposed model to predict unknown transition temperatures of Fe-based superconductors.
Highlights
Superconductors with the zero resistance and the Meissner effect have significant practical application [1]
The best known application is in the Magnetic Resonance Imaging (MRI) systems widely employed by health care professionals for detailed internal body imaging
The performance of the trained network is evaluated by the mean absolute error (MAE), the root mean square error (RMSE), and the correlation coefficient (CC)
Summary
Superconductors with the zero resistance and the Meissner effect have significant practical application [1]. The measurement of the transition temperature needs high precision devices including temperature controllers, constant current sources, and voltmeters, etc. These conditions cannot be achieved by ordinary laboratories. It is necessary to artificially operate liquid nitrogen (77K) in the measurement process, and there are certain security risks It mainly depends on liquid helium (4.2K) as refrigerant for superconductors that have strict temperature requirements. To avoid the strict measurement conditions and risk factors of the transition temperature measurement process, in this paper, we adopt a machine learning method to build a model based on the lattice parameters to predict the transition temperature of Fe-based superconductors
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