What is the likelihood that a hypothetical material - the combination of a composition and crystal structure - can be formed? Underpinning the reliability of predictions for local or global...
The Fermi surface is an important tool for understanding the electronic, optical, and magnetic properties of metals and doped semiconductors (Dugdale, 2016). It defines the surface in reciprocal space that divides unoccupied and occupied states at zero temperature. The topology of the Fermi surface impacts a variety of quantum phenomena including superconductivity, topological insulation, and ferromagnetism, and it can be used to predict the complex behaviour of systems without requiring more detailed computations. For example: (i) large nested Fermi sheets are a characteristic of charge density ordering (Lomer, 1962); (ii) the size and position of Fermi pockets are indicators of high-performance thermoelectrics (Park et al., 2020); and (iii) the average group velocities across the Fermi surface control the sensitivity of materials for dark matter detection (Inzani et al., 2021). IFermi is a Python library for the generation, analysis, and visualisation of Fermi surfaces that can facilitate sophisticated analyses of Fermi surface properties. * equal contribution † equal contribution Ganose et al., (2021). IFermi: A python library for Fermi surface generation and analysis.
Thermoelectric
materials offer the possibility of enhanced energy
efficiency due to waste heat scavenging. Based on their high-temperature
stability and ease of synthesis, efficient oxide-based thermoelectrics
remain a tantalizing research goal; however, their current performance
is significantly lower than the industry standards such as Bi2Te3 and PbTe. Among the oxide thermoelectrics studied
thus far, the development of n-type thermoelectric oxides has fallen
behind that of p-type oxides, primarily due to limitations on the
overall dimensionless figure of merit, or ZT, by
large lattice thermal conductivities. In this article, we propose
a simple strategy based on chemical intuition to discover enhanced
n-type oxide thermoelectrics. Using state-of-the-art calculations,
we demonstrate that the PbSb2O6-structured BaBi2O6 represents a novel structural motif for thermoelectric
materials, with a predicted ZT of 0.17–0.19.
We then suggest two methods to enhance the ZT up
to 0.22, on par with the current best earth-abundant n-type thermoelectric
at around 600 K, SrTiO3, which has been much more heavily
researched. Our analysis of the factors that govern the electronic
and phononic scattering in this system provides a blueprint for optimizing ZT beyond the perfect crystal approximation.
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