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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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TcESTIME: predicting high-temperature hydrogen-based superconductors.

Trinidad Novoa1,2, Matías E di Mauro1, Diego Inostroza1

  • 1Laboratoire de Chimie Théorique (LCT), Sorbonne Université, CNRS 4 Pl. Jussieu Paris 75005 France trinidad.novoa_aguirre@sorbonne-universite.fr.

Chemical Science
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

We developed TcESTIME, a code to rapidly predict high critical temperature (Tc) superconductivity in hydrogen-based materials by analyzing electronic structure. This tool enables faster screening of new superconductors, moving beyond costly computations and visual analysis.

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Area of Science:

  • Material Science
  • Condensed Matter Physics
  • Computational Chemistry

Background:

  • Superconductivity, a 20th-century discovery, faces challenges in synthesizing and characterizing new high critical temperature (Tc) materials.
  • Theoretical predictions are valuable but computationally expensive.
  • Existing methods for identifying superconductivity indicators like networking value and molecularity index rely on visual analysis, limiting scalability.

Purpose of the Study:

  • To develop a computational tool for high-throughput screening of hydrogen-based superconductors.
  • To automate the quantification of electronic structure features correlated with high Tc.
  • To establish a faster alternative to computationally intensive methods for predicting superconductivity.

Main Methods:

  • Developed the TcESTIME code, implementing periodic algorithms to calculate the networking value from electronic structure.
  • Utilized the Electron Localization Function topology to determine electron delocalization channels (networking value) and quantify molecular presence.
  • Applied TcESTIME to a dataset of hydrogen-based superconductors, including ternary compounds.

Main Results:

  • The TcESTIME code accurately estimates Tc for known superconductors like LaH10 in minutes.
  • New fits for Tc estimation were proposed, achieving an error of approximately 33 K.
  • Demonstrated the code's capability for high-throughput screening of potential superconducting materials.

Conclusions:

  • TcESTIME provides a scalable and efficient method for identifying potential high-Tc superconductors.
  • The code facilitates automated screening, reducing reliance on costly computations and subjective visual analysis.
  • This work lays the foundation for accelerated discovery of novel hydrogen-based superconductors.