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Computationally guided discovery of thermoelectric materials

Abstract

The potential for advances in thermoelectric materials, and thus solid-state refrigeration and power generation, is immense. Progress so far has been limited by both the breadth and diversity of the chemical space and the serial nature of experimental work. In this Review, we discuss how recent computational advances are revolutionizing our ability to predict electron and phonon transport and scattering, as well as materials dopability, and we examine efficient approaches to calculating critical transport properties across large chemical spaces. When coupled with experimental feedback, these high-throughput approaches can stimulate the discovery of new classes of thermoelectric materials. Within smaller materials subsets, computations can guide the optimal chemical and structural tailoring to enhance materials performance and provide insight into the underlying transport physics. Beyond perfect materials, computations can be used for the rational design of structural and chemical modifications (such as defects, interfaces, dopants and alloys) to provide additional control on transport properties to optimize performance. Through computational predictions for both materials searches and design, a new paradigm in thermoelectric materials discovery is emerging.

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Figure 1: Relationships between real and reciprocal space, and diversity of the search space.
Figure 2: Dependence of the scattering rate on energy.
Figure 3: High-throughput searches to identify promising n-type Zintl thermoelectrics.
Figure 4: Predicting lattice thermal conductivity.

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Acknowledgements

The authors acknowledge support from the US National Science Foundation DMR programme (Grant No. 1334713), the US Department of Energy (Contract No. DE-AC36-08GO28308), the National Renewable Energy Laboratory (NREL) and through NREL's LDRD programme (Grant No. 06591403).

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Correspondence to Prashun Gorai, Vladan Stevanović or Eric S. Toberer.

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Gorai, P., Stevanović, V. & Toberer, E. Computationally guided discovery of thermoelectric materials. Nat Rev Mater 2, 17053 (2017). https://doi.org/10.1038/natrevmats.2017.53

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