Predicting Anisotropic Performance in Thermoelectrics
In the first three years of this grant, we developed and
validated experimentally a computational prediction engine for
thermoelectric performance. Several new material classes emerged
from this search with excellent performance.
This prediction engine focused on isotropic properties,
but some materials exhibit anisotropic transport that yields
preferential directions for optimal performance.
Prediction engine extended to handle anisotropic
materials and identify new materials for single crystal growth. Known
materials with isotropic (eg. PbTe) and highly anisotropic (eg. SnSe)
performance successfully confirmed (Figure). Efforts are underway to
close the loop and grow new single crystals with excellent
performance along certain directions.