Screening Promising Thermoelectric Materials in Binary Chalcogenides through High-Throughput Computations.

Area of Science:

• Materials Science
• Computational Materials Science
• Solid State Physics

Background:

• High-throughput (HT) computational methods are crucial for identifying advanced functional materials.
• Screening for high-performance thermoelectric materials requires efficient predictive descriptors.

Purpose of the Study:

• To develop and apply novel descriptors for high-throughput screening of thermoelectric materials.
• To identify new binary chalcogenide candidates with excellent thermoelectric properties.

Main Methods:

• Deformation potential method and single band model to evaluate carrier relaxation time and establish electrical descriptor (χ).
• Elastic properties to efficiently calculate the Grüneisen parameter (γ), avoiding phonon calculations.
• High-throughput computation applied to 243 binary chalcogenides using descriptors χ and γ.

Main Results:

• Successfully screened 50 promising thermoelectric materials from 243 binary chalcogenides.
• Predicted 9 novel p-type and 14 novel n-type binary chalcogenides as promising thermoelectrics.
• Validated descriptors by predicting known promising thermoelectric materials.

Conclusions:

• The developed descriptors (χ and γ) enable efficient high-throughput screening of thermoelectric materials.
• Identified new binary chalcogenide candidates for future experimental investigation.
• The study provides reliable descriptors for accelerating the discovery of high-performance thermoelectric materials.