Machine learning doped MgB2 superconductor critical temperature from topological indices
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Yun Zhang
und
Xiaojie Xu
Abstract
Due to the absence of weak-links in grain boundaries, less anisotropy, and high availabilities at reasonable cost, magnesium boride, MgB2, has been studied extensively in the past decade. It has relatively high critical temperature, which is correlated to crystallographic and electronic structures. Two topological indices, the electric connectivity index and valence energy level connectivity, are characteristics of compound branching. We develop the Gaussian process regression (GPR) model to shed light on the relationship between topological descriptors and superconducting transition temperature for doped MgB2 superconductors. The model is highly accurate and stable, which contributes to fast predictions of superconducting transition temperature.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflict of interest.
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Data availability statement: Data used are available in the paper.
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Artikel in diesem Heft
- Frontmatter
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- Machine learning doped MgB2 superconductor critical temperature from topological indices
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- News
- DGM – Deutsche Gesellschaft für Materialkunde
Artikel in diesem Heft
- Frontmatter
- Original Papers
- Aluminium nitride dispersion strengthened steel
- Synthesis of spherical mullite/Yb2SiO5 composite EBC powder by using mechanical alloying and spray dry processes
- Absorber film deposition by hollow cathode discharge for solar thermal collectors application
- Linear and nonlinear optical properties of 1-(2-methoxyphenyl)-3-(4-chlorophenyl) triazene
- Machine learning doped MgB2 superconductor critical temperature from topological indices
- Investigation on an anti-corrosion Cu-rich multiple-principal-element alloy strengthened and toughened by nano-scaled L12-type ordered particles
- Study on the microstructure and age hardening capability in Al–Cu–Li alloys with different Cu/Li ratio
- News
- DGM – Deutsche Gesellschaft für Materialkunde
