Time Dependent Changes in Ag Doped YBCO Superconductors

Volochová, D.; Antal, V.; Diko, P.; Šefčiková, M.; Zmorayová, K.; Kováč, J.; Weber, H.W.; Chaud, X.

doi:10.12693/aphyspola.118.1047

Cited by 2 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…The best known application is in the Magnetic Resonance Imaging (MRI) systems widely employed by health care professionals for detailed internal body imaging. Other prominent applications include the magnetically levitated trains without friction and electrical power transmission with no energy loss [2][3][4][5]. However, superconductors have superconductivity only at or below their transition temperature [6], which hold back the wide spread application of superconductors.…”

Section: Introductionmentioning

confidence: 99%

Fe-based superconducting transition temperature modeling by machine learning: A computer science method

2021

PLoS ONE

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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.

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Section: Introductionmentioning

confidence: 99%

Fe-based superconducting transition temperature modeling by machine learning: A computer science method

2021

PLoS ONE

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“…As important functional materials, high-transitiontemperature (high-T C ) superconductors 1 have some typical physical parameters, such as transition temperature T C , magnetic susceptibility and critical current density (J C ), which make them very useful in many practical applications like magnetically levitated trains and power transmission. [2][3][4][5] Previous researches showed that the high-T C superconductors are generally characterized by a two-dimensional layered superconducting condensate with unique features that are not traditional superconducting metals. 6 Their important property, T C , is determined by their layered crystals, bond lengths, valency properties of the ions, and Coulomb coupling between electronic bands in adjacent, spatially separated layers.…”

Section: Introductionmentioning

confidence: 99%

Prediction of superconducting transition temperature using a machine-learning method

Liu¹,

Zhang²,

Xu³

et al. 2018

Mater. Tehnol.

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A high-transition-temperature (high-TC) superconductor is an important material used in many practical applications like magnetically levitated trains and power transmission. The superconducting transition temperature TC is determined by the layered crystals, bond lengths, valency properties of the ions and Coulomb coupling between electronic bands in adjacent, spatially separated layers. The optimal TC can be attained upon doping and applying the pressure for the optimal compounds. There is an algebraic relation for the optimal TC of the optimal compounds, TCO = KB-1 b/(ix), where i and x are two structural parameters, KB is Boltzmann's constant, b is a universal constant and TCO is the optimal transition temperature. Nevertheless, the TC of the non-optimum compounds is smaller than TCO. To predict the TC for the all compounds, we developed a prediction model based on the machine-learning method called support vector regression (SVR) using structural and electronic parameters to predict TC. In addition, the principal component analysis (PCA) was applied to reduce dimensions and interdependencies among the parameters, and particle swarm optimization (PSO) was utilized to search for the optimal parameters of SVR for an improved performance of the prediction model. The results showed that the proposed PCA-PSO-SVR model takes advantage of the machine-learning method to directly predict TC and theoretically provide guidance on measuring TC.

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Time Dependent Changes in Ag Doped YBCO Superconductors

Cited by 2 publications

References 8 publications

Fe-based superconducting transition temperature modeling by machine learning: A computer science method

Fe-based superconducting transition temperature modeling by machine learning: A computer science method

Prediction of superconducting transition temperature using a machine-learning method

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