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Fe-based superconducting transition temperature modeling by machine learning: A computer science method
- Source :
- PLoS ONE, Vol 16, Iss 8, p e0255823 (2021), PLoS ONE
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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.
- Subjects :
- High-temperature superconductivity
computer.software_genre
Helium
law.invention
Diagnostic Radiology
Machine Learning
law
Animal Cells
Lattice (order)
Condensed Matter::Superconductivity
Medicine and Health Sciences
Transition Temperature
Data Mining
Superconductors
Materials
Data Management
Superconductivity
Neurons
Crystallographic point group
Multidisciplinary
Crystallography
Applied Mathematics
Simulation and Modeling
Radiology and Imaging
Temperature
Magnetic Resonance Imaging
Chemistry
Physical Sciences
Medicine
Cellular Types
Algorithms
Research Article
Chemical Elements
Computer and Information Sciences
Field (physics)
Imaging Techniques
Science
Materials Science
Material Properties
Machine learning
Research and Analysis Methods
Machine Learning Algorithms
Artificial Intelligence
Diagnostic Medicine
Dopant
business.industry
Transition temperature
Doping
Biology and Life Sciences
Cell Biology
Cellular Neuroscience
Artificial intelligence
business
computer
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....03ae1404365ff21875319e7a367dee00