Simultaneous Optimization of Carrier Concentration and Alloy Scattering for Ultrahigh Performance GeTe Thermoelectrics

Li, Juan; Chen, Zhiwei; Zhang, Xinyue; Yu, Hulei; Wu, Zhixiong; Xie, Huaqing; Chen, Yue; Pei, Yanzhong

doi:10.1002/advs.201700341

Cited by 175 publications

(193 citation statements)

References 74 publications

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“…Indeed, it is found that the Hall carrier concentration ( n H ) linearly decreases from ~7 × 10 20 to ~2 × 10 20 /cm 3 with increasing x (Figure A), which can be roughly understood by the increased mean size of cations by PbTe alloying because of a decrease upon Pb/Sn substitution of the equilibrium cation vacancy concentration . Similar cation‐sized effect on carrier concentration has been found in GeTe thermoelectrics …”

Section: Resultssupporting

confidence: 66%

“…As the increase in mean size of cations accompanies with a reduction of carrier concentration in IV‐VI thermoelectrics, a reduction of carrier concentration is expected in Sn 0.8‐ x Mn 0.2 Pb x Te with increasing x . Indeed, it is found that the Hall carrier concentration ( n H ) linearly decreases from ~7 × 10 20 to ~2 × 10 20 /cm 3 with increasing x (Figure A), which can be roughly understood by the increased mean size of cations by PbTe alloying because of a decrease upon Pb/Sn substitution of the equilibrium cation vacancy concentration .…”

Section: Resultsmentioning

confidence: 99%

“…One noticeable challenging is that the increase of MnTe alloying leads to a significant deviation in carrier concentration from its optimum . An effective approach is inspired by the feature that the carrier concentration of IV‐VI tellurides is closely related to the size of cation . In more details, a gradual increase in cation size from Ge to Sn and then to Pb leads to a decrease in intrinsic carrier concentration from GeTe to SnTe and then to PbTe by orders of magnitude .…”

Section: Introductionmentioning

confidence: 99%

“…37 An effective approach is inspired by the feature that the carrier concentration of IV-VI tellurides is closely related to the size of cation. [66][67][68] In more details, a gradual increase in cation size from Ge to Sn and then to Pb leads to a decrease in intrinsic carrier concentration from GeTe to SnTe and then to PbTe by orders of magnitude. [69][70][71] This indicates that an increase in the mean size of cations such as by PbTe alloying might lead to an effective reduction in carrier concentration, an effect of which has been observed in GeTe with Pb/Ge substitutions 68 and SnTe-PbTe.…”

Section: Introductionmentioning

confidence: 99%

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Solute manipulation enabled band and defect engineering for thermoelectric enhancements of SnTe

Yao

Tang

et al. 2019

InfoMat

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With years of development, SnTe as a homologue of PbTe has shown great potential for thermoelectric applications in p‐type conduction, and the most successful strategy is typified by alloying for maximizing the valence band degeneracy. Among the known alloy agents, MnTe has been found to be one of the most effective enabling a band convergence for an enhancement in electronic performance of SnTe, yet its solubility of only ~15 at% unfortunately prevents a full optimization in the valence band structure. This work reveals that additional PbTe alloying not only promotes the MnTe solubility to locate the optimal valence band structure but also increases the overall substitutional defects in the material for a substantial reduction in lattice thermal conductivity. In addition, PbTe alloying simultaneously optimizes the carrier concentration due to the cation size effect. These features all enabled by such a solute manipulation synergistically lead to a very high thermoelectric figure of merit, zT of ~1.5 in SnTe with a 20 at% MnTe and a 30 at% PbTe alloying (Sn0.5Mn0.2Pb0.3Te), demonstrating the effectiveness of solute manipulation for advancing SnTe and similar thermoelectrics.

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Section: Resultssupporting

confidence: 66%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solute manipulation enabled band and defect engineering for thermoelectric enhancements of SnTe

Yao

Tang

et al. 2019

InfoMat

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“…[109] Extending their range of applications further, PCMs are also rapidly garnering interest as candidate materials for photonic devices such as switchable displays (Figure 3b) [110][111][112] or thermoelectrics for waste heat recovery (e.g., by alloying GeTe with guest atoms to modify the atomic and electronic structure). [113][114][115] Adv. Mater.…”

Section: Structure Dynamics and Function In Phase-change Materialsmentioning

confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

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Manipulation of Band Structure and Interstitial Defects for Improving Thermoelectric SnTe

Tang

Gao

Lin

et al. 2018

Adv Funct Materials

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191

166

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Many efforts are recently devoted on improving thermoelectric SnTe as an environment-friendly alternative to conventional PbTe and successful approaches include valence band convergence, nanostructuring, and substantial/interstitial defects. Among these strategies, alloying SnTe with MnTe enables the most effective reduction in the valence band offset (between L and Σ) for a convergence due to its high solubility of ≈15%, yet there is no indication that the solubility of MnTe is high enough for fully optimizing the valence band structure and thus for maximizing the electronic performance. Here, a strategy is shown to increase the MnTe solubility up to ≈25% by alloying with 5% GeTe, which successfully locates the composition (20% MnTe) to optimize the valence band structure by converging a more degenerated Λ (as compared with band L) and Σ valence bands. Through a further alloying with Cu 2 Te, the resultant Cu-interstitial defects enable a sufficient reduction in lattice thermal conductivity to its amorphous limit (0.4 W m −1 K −1 ). These electronic and thermal effects successfully realize a record-high thermoelectric figure of merit, zT of 1.8, strongly competing with that of PbTe. This work demonstrates the validity of band manipulation and interstitial defects for realizing extraordinary thermoelectric performance in SnTe.

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Simultaneous Optimization of Carrier Concentration and Alloy Scattering for Ultrahigh Performance GeTe Thermoelectrics

Cited by 175 publications

References 74 publications

Solute manipulation enabled band and defect engineering for thermoelectric enhancements of SnTe

Solute manipulation enabled band and defect engineering for thermoelectric enhancements of SnTe

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Manipulation of Band Structure and Interstitial Defects for Improving Thermoelectric SnTe

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