Fe-based superconducting transition temperature modeling by machine learning: A computer science method.
| Autor: | Hu Z; China University of Mining and Technology Beijing, Beijing, China. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | PloS one [PLoS One] 2021 Aug 06; Vol. 16 (8), pp. e0255823. Date of Electronic Publication: 2021 Aug 06 (Print Publication: 2021). |
| DOI: | 10.1371/journal.pone.0255823 |
| Abstrakt: | 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. Competing Interests: The author have declared that no competing interests exist. |
| Databáze: | MEDLINE |
| Externí odkaz: |
|