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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.
The design of uranium-based thermoelectric materials presents a novel and intriguing strategy for directly converting nuclear heat into electrical power. Using high-level first-principles approach combined with accurate solution of Boltzmann transpor
Half-Heusler alloys (MgAgSb structure) are promising thermoelectric materials. RNiSn half-Heusler phases (R=Hf, Zr, Ti) are the most studied in view of their thermal stability. The highest dimensionless figure of merit (ZT) obtained is ~1 in the temp
Thermoelectric (TE) conversion in conducting materials is of eminent importance for providing renewable energy and solid-state cooling. Although traditionally, the Seebeck effect plays a key role for the TE figure of merit zST, it encounters fundamen
Dimensionless thermoelectric figure of merit $ZT$ is investigated for two-dimensional organic conductors $tau-(EDO-S,S-DMEDT-TTF)_2(AuI_2)_{1+y}$, $tau$-(EDT-S,S-DMEDT-TTF)_2(AuI_2)_{1+y}$ and $tau$-(P-S,S-DMEDT-TTF)_2(AuI_2)_{1+y}$ ($y le 0.875$), r
The influence of periodic edge vacancies and antidot arrays on the thermoelectric properties of zigzag graphene nanoribbons is investigated. Using the Greens function method, the tight-binding approximation for the electron Hamiltonian and the 4th ne