TcESTIME: predicting high-temperature hydrogen-based superconductors

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.