Symmetry-Forbidden p–d Orbital Mixing: A Pathway to High-Power-Factor Thermoelectrics
Source abstract
Abstract
The intrinsic entanglement between electrical conductivity (σ) and the Seebeck coefficient ( S ) poses a significant challenge to enhancing the power factor (PF) of thermoelectric (TE) materials. While achieving high valley degeneracy ( N vk ) is an effective strategy for balancing σ and S, thus improving PF, identifying a compound with a high N vk ( N vk ≥ 6) remains a complex endeavor. In this study, we propose an effective approach to quickly identify p-type semiconductors with high N vk through analyzing the mixing between anion p and cation d orbitals. By prohibiting the p–d orbital mixing at the Γ point, we can effectively shift the valence-band maximum away from the Brillouin zone center (Γ point, N vk = 1), thereby increasing N vk . Through the examination of common irreducible representations of anion p and cation d orbitals at the Γ point, we screen and discover six compounds with N vk ≥ 6 from 921 binary and ternary semiconductors. First-principles calculations incorporating electron–phonon coupling (EPC) demonstrate that PtP 2, PtAs 2, and PtS 2 exhibit exceptionally high PF values of 130, 127, and 82 μW cm –1 K –2 at 300 K, respectively, which are 3–5 times higher than those of traditional high-performance TE materials. Furthermore, the weighted mobilities (μ w ) of PtP 2, PtAs 2, and PtS 2 at room temperature are also high, with values of 952, 906, and 304 cm 2 V –1 s –1, respectively. Our work not only enhances the understanding of high N vk formation through group theory but also presents an effective and efficient methodology for the discovery of materials with high PF. Additionally, this approach is versatile and can be extended to more complicated systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Thermoelectric Materials and Devices · Machine Learning in Materials Science · 2D Materials and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Advanced Materials and Devices for Post-Moore Chips, Ministry of Education
University of Science and Technology Beijing
School of Mathematics and Physics
Funding
National Natural Science Foundation of China · University of Science and Technology Beijing
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