Predicting Anisotropic Performance in Thermoelectrics

Background: 
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.
Opportunity: 
This prediction engine focused on isotropic properties, 
but   some   materials   exhibit   anisotropic   transport   that   yields 
preferential directions for optimal performance.
Outcome: 
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.