The Mg 3 Sb 2− x Bi x family has emerged as the potential candidates for thermoelectric applications due to their ultra-low lattice thermal conductivity ( κ L ) at room temperature (RT) and structural complexity. Here, using ab initio calculations of the electron-phonon averaged (EPA) approximation coupled with Boltzmann transport equation (BTE), we have studied electronic, phonon and thermoelectric properties of Mg 3 Sb 2− x Bi x (x = 0, 1, and 2) monolayers. In violation of common mass-trend expectations, increasing Bi element content with heavier Zintl phase compounds yields an abnormal change in κ L in two-dimensional Mg 3 Sb 2− x Bi x crystals at RT (∼0.51, 1.86, and 0.25 W/mK for Mg 3 Sb 2 , Mg 3 SbBi, and Mg 3 Bi 2 ). The κ L trend was detailedly analyzed via the phonon heat capacity, group velocity and lifetime parameters. Based on quantitative electronic band structures, the electronic bonding through the crystal orbital Hamilton population (COHP) and electron local function analysis we reveal the underlying mechanism for the semiconductor-semimetallic transition of Mg 3 Sb 2-− x Bi x compounds, and these electronic transport properties (Seebeck coefficient, electrical conductivity, and electronic thermal conductivity) were calculated. We demonstrate that the highest dimensionless figure of merit ZT of Mg 3 Sb 2− x Bi x compounds with increasing Bi content can reach ∼1.6, 0.2, and 0.6 at 700 K, respectively. Our results can indicate that replacing heavier anion element in Zintl phase Mg 3 Sb 2− x Bi x materials go beyond common expectations (a heavier atom always lead to a lower κ L from Slack’s theory), which provide a novel insight for regulating thermoelectric performance without restricting conventional heavy atomic mass approach.
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Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium
Abstract Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, but assumptions about the system temperatures (i.e., small temperature gradients) are usually made to ensure that it is computationally tractable. To include the effects of large temperature non-equilibrium, we demonstrate a data-free deep learning scheme, physics-informed neural network (PINN), for solving stationary, mode-resolved phonon BTE with arbitrary temperature gradients. This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables. Numerical experiments suggest that the proposed PINN can accurately predict phonon transport (from 1D to 3D) under arbitrary temperature gradients. Moreover, the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.
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- NSF-PAR ID:
- 10338465
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41524-022-00712-y
