Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMachine-Learning-based Prediction of Lattice Thermal Conductivity for Half-Heusler Compounds using Atomic Information
155
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. This is because it has various electronic structures, such as semi-metals, semiconductors, and a topological insulator. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity, which is an important physical parameter for controlling the thermal management of the device, requires a calculation cost of several 100 times as much as the usual density functional theory calculation. Therefore, we examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory calculation in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials.
rate research
Read More
Since their discovery around a century ago, the structure and chemistry of the multi-functional half-Heusler semiconductors have been studied extensively as three component systems. The elemental groups constituting these ternary compounds with the nominal formula XYZ are well established. From the very same set of well-known elements we explore a phase space of quaternary double ($XXY_2Z_2$, $X_2YYZ_2$, and $X_2Y_2ZZ$), triple ($X_2XY_3Z_3$) and quadruple ($X_3XY_4Z_4$) half-Heusler compositions which 10 times larger in size. Using a reliable, first-principles thermodynamics methodology on a selection of 347 novel compositions, we predict 127 new stable quaternary compounds, already more than the 89 reported almost exhaustively for ternary systems. Thermoelectric performance of the state-of-the-art ternary half-Heusler compounds are limited by their intrinsically high lattice thermal conductivity ($kappa_{L}$). In comparison to ternary half-Heuslers, thermal transport in double half-Heuslers is dominated by low frequency phonon modes with smaller group velocities and limited by disorder scattering. The double half-Heusler composition Ti$_2$FeNiSb$_2$ was synthesized and confirmed to have a significantly lower lattice thermal conductivity (factor of 3 at room temperature) than TiCoSb, thereby providing a better starting point for thermoelectric efficiency optimization. We demonstrate a dependable strategy to assist the search for low thermal conductivity half-Heuslers and point towards a huge composition space for implementing it. Our findings can be extended for systematic discovery of other large families of multi-component intermetallic semiconductors.
Half-Heusler compounds usually exhibit relatively higher lattice thermal conductivity that is undesirable for thermoelectric applications. Here we demonstrate by first-principles calculations and Boltzmann transport theory that the BiBaK system is an exception, which has rather low thermal conductivity as evidenced by very small phonon group velocity and relaxation time. Detailed analysis indicates that the heavy Bi and Ba atoms form a cage-like structure, inside which the light K atom rattles with larger atomic displacement parameters. In combination with its good electronic transport properties, the BiBaK shows a maximum n-type ZT value of 1.9 at 900 K, which outperforms most half-Heusler thermoelectric materials.
The factors that affect the thermal conductivity of semiconductors is a topic of great scientific interest, especially in relation to thermoelectrics. Key developments have been the concept of the phonon-glass-electron-crystal (PGEC) and the related idea of rattling to achieve this. We use first principles phonon and thermal conductivity calculations in order to explore the concept of rattling for stoichiometric ordered half-Heusler compounds. These compounds can be regarded as filled zinc blende materials, and the filling atom could be viewed as a rattler if it is weakly bound. We use two simple metrics, one related to the frequency and the other to bond frustration and anharmonicity. We find that both measures correlate with thermal conductivity. This suggests that both may be useful in screening materials for low thermal conductivity.
The XYZ half-Heusler crystal structure can conveniently be described as a tetrahedral zinc blende YZ structure which is stuffed by a slightly ionic X species. This description is well suited to understand the electronic structure of semiconducting 8-electron compounds such as LiAlSi (formulated Li$^+$[AlSi]$^-$) or semiconducting 18-electron compounds such as TiCoSb (formulated Ti$^{4+}$[CoSb]$^{4-}$). The basis for this is that [AlSi]$^-$ (with the same electron count as Si$_2$) and [CoSb]$^{4-}$ (the same electron count as GaSb), are both structurally and electronically, zinc-blende semiconductors. The electronic structure of half-metallic ferromagnets in this structure type can then be described as semiconductors with stuffing magnetic ions which have a local moment: For example, 22 electron MnNiSb can be written Mn$^{3+}$[NiSb]$^{3-}$. The tendency in the 18 electron compound for a semiconducting gap -- believed to arise from strong covalency -- is carried over in MnNiSb to a tendency for a gap in one spin direction. Here we similarly propose the systematic examination of 18-electron hexagonal compounds for semiconducting gaps; these would be the stuffed wurtzite analogues of the stuffed zinc blende half-Heusler compounds. These semiconductors could then serve as the basis for possibly new families of half-metallic compounds, attained through appropriate replacement of non-magnetic ions by magnetic ones. These semiconductors and semimetals with tunable charge carrier concentrations could also be interesting in the context of magnetoresistive and thermoelectric materials.
Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of thermal conductivities which are numerically expensive. In this work, we applied the recently developed machine learning interatomic potential (MLIP) to evaluate the thermal conductivity of hexagonal boron nitride monolayers. The MLIP is obtained using the Gaussian approximation potential (GAP) method, and the resulting lattice dynamical properties and thermal conductivity are compared with those obtained from explicit frozen phonon calculations. It is observed that accurate thermal conductivity can be obtained based on MLIP constructed with about 30% representative configurations, and the high-order force constants provide a more reliable benchmark on the quality of MLIP than the harmonic approximation.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
