Entropy as a Gene-Like Performance Indicator Promoting Thermoelectric Materials

5 years ago

Entropy as a Gene-Like Performance Indicator Promoting Thermoelectric Materials

Ruiheng Liu, Xun Shi, Hongyi Chen, Tiansong Zhang, Binbin Jiang, Yuting Qin, Kunpeng Zhao, Wenqing Zhang, Ctirad Uher, Lidong Chen, Gang Sha

High-throughput explorations of novel thermoelectric materials based on the Materials Genome Initiative paradigm only focus on digging into the structure-property space using nonglobal indicators to design materials with tunable electrical and thermal transport properties. As the genomic units, following the biogene tradition, such indicators include localized crystal structural blocks in real space or band degeneracy at certain points in reciprocal space. However, this nonglobal approach does not consider how real materials differentiate from others. Here, this study successfully develops a strategy of using entropy as the global gene-like performance indicator that shows how multicomponent thermoelectric materials with high entropy can be designed via a high-throughput screening method. Optimizing entropy works as an effective guide to greatly improve the thermoelectric performance through either a significantly depressed lattice thermal conductivity down to its theoretical minimum value and/or via enhancing the crystal structure symmetry to yield large Seebeck coefficients. The entropy engineering using multicomponent crystal structures or other possible techniques provides a new avenue for an improvement of the thermoelectric performance beyond the current methods and approaches. Entropy as a global gene-like performance indicator is validatied by screening multicomponent thermoelectric materials with high entropy, and can be designed via a high-throughput approach. Optimizing entropy can lead to greatly improved thermoelectric performance through either a significantly depressed lattice thermal conductivity down to its theoretical minimum value and/or via enhancing the structure symmetry to yield large Seebeck coefficients.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/adma.201702712