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New high-throughput search strategies based on statistical identification of promising chemical subspaces show potential for accelerating half-Heusler thermoelectric materials discovery. 

Abstract Superionic conductors, includingACrX₂(A=Ag, Cu; X = S, Se) compounds, have attracted attention due to their low lattice thermal conductivity and high ionic conductivity. These properties are driven by structural characteristics such as anharmonicity, soft bonding, and disorder, which enhance both fast ion transport and thermal resistance. In the present study, we investigate the impact of various factors (e.g.A-site disorder, microstructure, speed of sound and chemical composition) on the thermal conductivity of the compounds CuCrS₂, CuCrSe₂, AgCrS₂and AgCrSe₂. The samples were synthesized using solid state reaction, ball milling and subsequent spark plasma sintering, and thermal diffusivity, electrical resistivity, Hall coefficients and Seebeck coefficients were measured as a function of temperature. The selenides were found to behave as degenerate semiconductors, with reasonable thermoelectric figure of merit (up to 0.79 in CuCrSe₂), while the sulfides behaved as non-degenerate semiconductors with high electrical resistivity. At room temperature, all samples are in the ordered phase and show low lattice thermal conductivity ranging from 0.60 W m⁻¹-K in AgCrSe₂to 1.1 W m⁻¹-K in CuCrSe₂. Little reduction in lattice thermal conductivity was observed in the high-temperature phase, despite the increased disorder on the cation site and the onset of superionic conductivity. This suggests that the low lattice thermal conductivity inACrX₂compounds is an inherent property of the crystal structure, caused by anharmonic bonding and diffuson dominated transport. 

The solid solution behavior of ZnTe in CuInTe2 complicates defect analysis. 

Bi1−xSbx alloys are classic thermoelectric materials for near-cryogenic applications. Despite more than half a century of study, unraveling the underlying transport physics within this space has been nontrivial due to the complex electronic structure, disorder, and small bandgap within these alloys. Furthermore, as Peltier coolers, Bi1−xSbx alloys operate in a bipolar regime; as such, understanding the impact of minority carriers is critical for further improvements in device performance. This study unites first principles calculations with low-temperature experimental measurements to create a generalized model for transport within semiconducting Bi-Sb alloys. Our exploration reveals the interplay between the complex, degenerate valence band structure with the extremely light conduction bands. By building a hybrid computational/experimental model, an understanding of both the electron and hole relaxation times emerges both as a function of temperature and energy. Special quasi-random supercell calculations reveal that, despite significant atomic disorder, the electronic band structures within the alloy remains largely unaffected and electron–phonon scattering dominates. For charge carriers near the band edges, the relaxation times are thus extremely long, consistent with cyclotronic behavior appearing at low magnetic fields (≪ 1 T). Modeling thermoelectric performance suggests that the valence band edge deformation potential is significantly weaker and highlights the potential for p-type compositions to meet or exceed the current n-type alloys. 

Stability prediction is accelerated by treating the convex hull as a probabilistic object, allowing for an efficient active learning process that minimizes the number of thermodynamic calculations necessary to define the convex hull. 

Active learning is a valuable tool for efficiently exploring complex spaces, finding a variety of uses in materials science. However, the determination of convex hulls for phase diagrams does not neatly fit into traditional active learning approaches due to their global nature. Specifically, the thermodynamic stability of a material is not simply a function of its own energy, but rather requires energetic information from all other competing compositions and phases. Here we present Convex hull-aware Active Learning (CAL), a novel Bayesian algorithm that chooses experiments to minimize the uncertainty in the convex hull. CAL prioritizes compositions that are close to or on the hull, leaving significant uncertainty in other compositions that are quickly determined to be irrelevant to the convex hull. The convex hull can thus be predicted with significantly fewer observations than approaches that focus solely on energy. Intrinsic to this Bayesian approach is uncertainty quantification in both the convex hull and all subsequent predictions (e.g., stability and chemical potential). By providing increased search efficiency and uncertainty quantification, CAL can be readily incorporated into the emerging paradigm of uncertainty-based workflows for thermodynamic prediction.