  Thermoelectric (TE) materials are among very few sustainable yet feasible
energy solutions of present time. This huge promise of energy harvesting is
contingent on identifying/designing materials having higher efficiency than
presently available ones. However, due to the vastness of the chemical space of
materials, only its small fraction was scanned experimentally and/or
computationally so far. Employing a compressed-sensing based symbolic
regression in an active-learning framework, we have not only identified a trend
in materials' compositions for superior TE performance, but have also predicted
and experimentally synthesized several extremely high performing novel TE
materials. Among these, we found Ag$_{0.55}$Cu$_{0.45}$GaTe$_2$ to possess an
experimental figure of merit as high as ~2.8 at 827 K, which is a breakthrough
in the field. The presented methodology demonstrates the importance and
tremendous potential of physically informed descriptors in material science, in
particular for relatively small data sets typically available from experiments
at well-controlled conditions.
