Rethinking SnSe Thermoelectrics from Computational Materials Science.

Area of Science:

• Materials Science
• Solid State Physics
• Computational Materials Science

Background:

• Growing energy crisis and environmental concerns drive demand for sustainable energy solutions.
• Thermoelectric technology offers direct heat-to-electricity conversion with zero emissions and long service life.
• Traditional thermoelectrics have performance limitations due to narrow bandgaps, restricting their operating temperature range.

Purpose of the Study:

• To explore and develop high-performance thermoelectric tin selenide (SnSe) crystals.
• To investigate strategies for enhancing both p-type and n-type SnSe for efficient energy conversion.
• To leverage computational materials science for designing advanced thermoelectric materials.

Main Methods:

• Utilized electronic structure calculations and multiband simulations for p-type SnSe optimization.
• Applied defect chemistry and point-defect calculations to tune p-type SnSe properties.
• Analyzed charge density and calculated deformation potential for n-type SnSe, focusing on charge and phonon transport decoupling.

Main Results:

• Achieved an ultrahigh power factor (PF) of ~75 μW cm⁻¹ K⁻² and ZTave of ~1.9 for p-type Sn$_{0.91}$Pb$_{0.09}$Se.
• Further enhanced p-type SnCu$_{0.001}$Se to PF > 100 μW cm⁻² and ZT ~1.5 by controlling intrinsic defects.
• Developed Pb-alloyed and Cl-doped SnSe (SnSe-Cl-PbSe) with ZTave ~1.7 across a wide temperature range (300–773 K).

Conclusions:

• Layered SnSe crystals are promising lead-free thermoelectric materials due to their wide bandgap and ultralow thermal conductivity.
• Computational approaches, including high-throughput calculations (HTC) and machine learning (ML), can accelerate thermoelectric material discovery.
• The developed strategies offer a pathway for designing next-generation thermoelectric materials and devices for sustainable energy applications.