Entropy as a Gene-Like Performance Indicator Promoting Thermoelectric Materials.

Area of Science:

• Materials Science
• Solid State Physics
• Computational Materials Design

Background:

• Current thermoelectric material discovery relies on non-global indicators, limiting comprehensive property prediction.
• Existing methods often overlook the holistic behavior of complex materials, hindering optimal design.

Purpose of the Study:

• To develop a strategy for designing high-performance thermoelectric materials using entropy as a global indicator.
• To demonstrate the effectiveness of high-throughput screening guided by entropy for novel thermoelectric discovery.

Main Methods:

• Utilized a high-throughput screening approach.
• Employed entropy as a gene-like performance indicator for material design.
• Investigated multicomponent crystal structures for entropy optimization.

Main Results:

• Successfully developed a strategy using entropy for designing multicomponent thermoelectric materials.
• Showcased that optimizing entropy significantly enhances thermoelectric performance.
• Achieved depressed lattice thermal conductivity and enhanced Seebeck coefficients through entropy engineering.

Conclusions:

• Entropy engineering offers a novel and effective avenue for improving thermoelectric performance.
• This approach provides a global perspective, overcoming limitations of non-global indicators.
• The findings pave the way for designing advanced thermoelectric materials beyond current methodologies.