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Related Concept Videos

Types Of Superconductors01:28

Types Of Superconductors

980
A superconductor is a substance that offers zero resistance to the electric current when it drops below a critical temperature. Zero resistance is not the only interesting phenomenon as materials reach their transition temperatures. A second effect is the exclusion of magnetic fields. This is known as the Meissner effect. A light, permanent magnet placed over a superconducting sample will levitate in a stable position above the superconductor. High-speed trains that levitate on strong...
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Superconductor01:24

Superconductor

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A substance that reaches superconductivity, a state in which magnetic fields cannot penetrate, and there is no electrical resistance, is referred to as a superconductor. In 1911, Heike Kamerlingh Onnes of Leiden University, a Dutch physicist, observed a relation between the temperature and the resistance of the element mercury. The mercury sample was then cooled in liquid helium to study the linear dependence of resistance on temperature. It was observed that, as the temperature decreased, the...
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Ferromagnetism01:31

Ferromagnetism

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Materials like iron, nickel, and cobalt consist of magnetic domains, within which the magnetic dipoles are arranged parallel to each other. The magnetic dipoles are rigidly aligned in the same direction within a domain by quantum mechanical coupling among the atoms. This coupling is so strong that even thermal agitation at room temperature cannot break it. The result is that each domain has a net dipole moment. However, some materials have weaker coupling, and are ferromagnetic at lower...
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Theory of Metallic Conduction01:17

Theory of Metallic Conduction

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The conduction of free electrons inside a conductor is best described by quantum mechanics. However, a classical model makes predictions close to the results of quantum mechanics. It is called the theory of metallic conduction.
In this theory, Newton's second law of motion is used to determine the acceleration of an electron in the presence of an applied electric field. Then, its velocity is expressed via this acceleration.
An electron moves through the crystal, containing positive ions,...
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Heating and Cooling Curves02:44

Heating and Cooling Curves

22.8K
When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
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Predicting superconducting transition temperature through advanced machine learning and innovative feature

Hassan Gashmard1, Hamideh Shakeripour2, Mojtaba Alaei1

  • 1Department of Physics, Isfahan University of Technology, Isfahan, 84156-83111, Iran.

Scientific Reports
|February 17, 2024
PubMed
Summary

Artificial intelligence (AI) accelerates the discovery of new superconducting materials by predicting their critical temperature (Tc). This study developed novel AI tools and a web application, achieving superior prediction accuracy for room-temperature superconductors.

Keywords:
CatBoostFourth paradigmsJabirMachine learningSorayaSuperconductorTransition temperature

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Area of Science:

  • Condensed matter physics
  • Materials science
  • Computational physics

Background:

  • Superconductivity offers revolutionary potential for energy technologies but achieving room-temperature superconductivity remains a significant challenge.
  • Artificial Intelligence (AI) is emerging as a powerful tool for accelerating the discovery of novel superconducting materials.
  • Predicting the transition temperature (Tc) is crucial for screening potential superconductors.

Purpose of the Study:

  • To develop and apply advanced AI methods for predicting the transition temperatures (Tc) of superconducting materials.
  • To create novel computational tools for feature generation and selection in materials science.
  • To build a user-friendly web application for predicting superconducting properties.

Main Methods:

  • Utilized the comprehensive SuperCon dataset, processed into the DataG dataset (13,022 compounds).
  • Applied the CatBoost algorithm for predicting transition temperatures.
  • Developed the Jabir package for generating 322 atomic descriptors and the Soraya package for hybrid feature selection.

Main Results:

  • Achieved high prediction accuracy with R² = 0.952 and RMSE = 6.45 K, outperforming previous literature results.
  • Successfully generated and selected critical features for robust superconducting property prediction.
  • Developed a functional web application for accessible Tc prediction.

Conclusions:

  • The developed AI-driven approach significantly enhances the prediction of superconducting transition temperatures.
  • Novel feature generation and selection methods improve the efficiency and accuracy of materials discovery.
  • The created web application democratizes access to predictive tools for superconducting materials research.