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LiCdSb, a half-Heusler semiconductor, is predicted to have ultralow lattice thermal conductivity (0.24 W/mK at 300 K) and a thermoelectric figure of merit above 1 beyond 600 K, via a machine-learned interatomic potential.

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2026-08-01 03:18 UTC pith:AMET7IOR

load-bearing objection Unquantified ZT>1 claim and placeholder references make this an unfinished draft, despite a sensible MLIP-based Kl workflow. the 4 major comments →

arxiv 2607.23203 v1 pith:AMET7IOR submitted 2026-07-25 cond-mat.mtrl-sci

A DFT and Machine Learning-Assisted Study on the Lattice Thermal Conductivity of LiCdSb for Thermoelectric Applications

R. Zosiamliana , Lalhriat Zuala , N. T. Tien , Vo Khuong Dien , A. Laref , D. P. Rai This is my paper
classification cond-mat.mtrl-sci
keywords half-Heuslerlattice thermal conductivitymachine-learned interatomic potentialthermoelectric figure of merit ZTLiCdSbdensity functional theoryBoltzmann transporthybrid functional
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper is trying to establish that LiCdSb, a lithium-cadmium-antimony half-Heusler compound, is a promising high-temperature thermoelectric material because its lattice thermal conductivity is very low. Using density functional theory with a hybrid functional for an accurate band gap, and a machine-learned interatomic potential to compute lattice thermal conductivity cheaply, the authors obtain a room-temperature value of 0.24 W/mK and a thermoelectric figure of merit ZT that rises above the benchmark of 1 beyond 600 K. The paper matters because high-temperature waste-heat recovery needs materials with high ZT and low thermal conductivity, and a non-toxic, earth-abundant candidate would be valuable. It also demonstrates a cost-saving pipeline: replace expensive anharmonic phonon calculations with a machine-learned potential, yielding results in qualitative agreement with experiment at 300 K.

Core claim

On the paper's own terms, the central discovery is that LiCdSb combines a HSE06-corrected electronic structure—a direct gap of 0.92 eV with light electrons and coexisting light and heavy holes—with a machine-learning-derived lattice thermal conductivity of 0.24 W/mK at room temperature, low enough that the thermoelectric figure of merit ZT = S²σT/(κe+κl) reaches values well above 1 for temperatures beyond 600 K. The authors argue that the ML-based κl reproduces the experimental ZT at 300 K (computed 0.17 vs experimental 0.10) more closely than the standard Slack model, with the agreement ranking ML + HSE06 > Slack+TDEC + HSE06 > Slack + HSE06. If correct, LiCdSb is an ultralow-κl half-Heusle

What carries the argument

The load-bearing machinery is the on-the-fly machine-learned interatomic potential (MLIP): a potential trained on 10,000 ab initio molecular dynamics steps at 300 K (10 ps), from which second- and third-order interatomic force constants are extracted and fed into the phonon Boltzmann transport equation to obtain κl. This replaces the expensive DFT-based anharmonic force-constant calculation. Alongside it, the paper couples a hybrid HSE06 band structure with Boltzmann transport for electronic coefficients, and uses a temperature-dependent elastic-constant (TDEC, quasi-static) extension of the Slack model as a cheaper cross-check. The MLIP is the piece doing the central work: it produces the u

Load-bearing premise

The paper's prediction of ultralow lattice thermal conductivity, and hence ZT>1 above 600 K, rests on the assumption that the machine-learned interatomic potential trained on only 10 ps of 300 K ab initio molecular dynamics remains accurate for anharmonic phonon-phonon interactions across the full 300-900 K range.

What would settle it

Measure the lattice thermal conductivity of LiCdSb at 600-900 K experimentally (e.g., on a dense polycrystalline pellet via laser flash), or compute κl from DFT-quality third-order interatomic force constants at high temperature; if the measured/computed κl comes out above about 0.5 W/mK in that range, the ZT>1 prediction collapses. A cheaper check: run the same MLIP workflow with AIMD data points at 700 K and 900 K and compare force predictions to fresh DFT forces.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If κl = 0.24 W/mK holds, LiCdSb's lattice thermal conductivity is among the lowest computed for half-Heuslers, making the material a candidate for thermoelectric generators operating on waste heat above 600 K.
  • The ML-assisted pipeline shows that machine-learned interatomic potentials trained on short AIMD trajectories can substitute for direct DFT anharmonic phonon calculations, making high-throughput screening of thermoelectric materials more feasible.
  • The computed ZT at 300 K (0.17) sits close to the experimental value (0.10) when HSE06 electronic structure is combined with the ML κl, indicating that both electronic-structure accuracy and lattice-transport accuracy are needed for quantitative predictions.
  • Below about 600 K the material's ZT is modest, so the payoff is specifically in high-temperature applications; doping or alloying that preserves the low κl could push ZT further.
  • The temperature-dependent elastic analysis (TDEC) supports the thermal stability picture, although the paper flags that the quasi-harmonic elastic constants behave anomalously; this tempers but does not remove the conclusion.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Inference: The ZT>1 claim relies on the MLIP extrapolating to 900 K from a 300 K training set; a transferability test (e.g., a handful of AIMD forces at 600-900 K) would settle whether the anharmonicity is genuinely captured or under-captured.
  • Inference: The paper's qualitative 300 K agreement (0.17 vs 0.10 experimental ZT) leaves room for the true high-temperature ZT to be lower; the plotted ZT>1 region may be an upper bound unless the carrier concentration is optimized—the paper does not report the chemical potential or doping level used in Figure 11.
  • Inference: Because LiCdSb is a Zintl-type half-Heusler with underbonded Cd, the same MLIP workflow could be applied to isoelectronic siblings (LiZnSb, LiMgSb, etc.) to see whether ultralow κl is a family trait rather than a single-compound accident.
  • Inference: A direct experimental measurement of κl on a single crystal at 300-900 K would be the cleanest test; if κl rises above about 0.5 W/mK at temperature, the ZT>1 claim would not survive.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This manuscript presents a first-principles study of the half-Heusler compound LiCdSb. Structural, electronic, elastic, and thermodynamic properties are computed with DFT (GGA, GGA+SOC, HSE06), and thermoelectric transport coefficients are obtained with BoltzTraP2 under both CRTA and RTA. Lattice thermal conductivity is estimated with three approaches: the standard Slack model, a Slack model extended with temperature-dependent elastic constants (TDEC), and an on-the-fly machine-learned interatomic potential (MLIP). The authors report a room-temperature Kl of 0.24 W m^-1 K^-1, ZT values of 0.17-0.18 at 300 K, and claim that ZT exceeds ~1 above 600 K, making LiCdSb potentially promising for high-temperature thermoelectric applications.

Significance. If substantiated, the claim of ZT > 1 above 600 K would be a significant result for half-Heusler thermoelectrics, especially because the available experimental ZT is only ~0.10 at 300 K. The computational pipeline combining HSE06 electronic structure with MLIP-based thermal transport is modern and, in principle, well suited to this problem. The MLIP force/energy RMSEs are encouraging, and the comparison with experimental ZT at 300-500 K is a useful benchmark. However, the paper currently does not provide the quantitative inputs needed to assess the central claim: no numerical ZT values above 500 K, no carrier concentration or chemical potential, and no demonstration that the MLIP trained at 300 K is transferable to 900 K. The significance is therefore potential rather than established.

major comments (4)
  1. [Abstract and §3.3, Fig. 11] The central claim 'ZT well above the benchmark value of ~1 beyond 600 K' is never quantified. The text and Figure 11 give no numerical ZT values above 500 K, no chemical potential or carrier concentration at which the curves are evaluated, and no statement whether the plotted ZT is the maximum over chemical potential. Since Eq. (8) defines all transport coefficients as functions of μ and the p-/n-type labels are not tied to a specific μ or carrier density, the high-temperature claim is not reproducible. Please report the peak ZT, the corresponding μ or carrier concentration, and the temperature for each functional and κl model.
  2. [§2.2 and Fig. 2] The MLIP is trained on 10,000 AIMD steps (10 ps) at 300 K only, but is used in Phono3py to compute second- and third-order IFCs and Kl over 300-900 K. The force/energy RMSEs of 0.010 eV/Å and 0.032 eV are parity metrics on training configurations; they do not establish transferability of the anharmonic potential surface to high temperature or to displaced configurations far from the training set. Please validate the MLIP phonon dispersions and Kl against direct DFT/DFPT or Phono3py results at least at 300 K with independent supercells, and ideally at elevated temperatures. Also report convergence of the 4×4×4 supercell and the 30×30×30 q-point mesh.
  3. [§3.3, Eq. (10), Fig. S5] The RTA transport results and all ZT values obtained with finite relaxation time depend on the deformation potential constants Ed, effective masses, and elastic constants through Eq. (10). The numerical values of Ed for the CBM and VBM are not reported, and the main text does not give the actual τ(T) values or the carrier concentration used. Without these inputs, the RTA panels in Figure 11 and the comparison with experiment cannot be reproduced or independently checked. Provide a table of Ed, τ0/τ(T), and the μ or carrier concentration used for each panel.
  4. [§3.2, Fig. 5] The rejection of the QHA results because C44 becomes negative near ~600 K is not sufficiently justified. The paper argues that no phase transition is known, but a negative C44 in QHA can also indicate a numerical/methodological artifact or a genuine tendency toward mechanical instability that a static QSA would miss. Since the Slack+TDEC Kl curve and the corresponding ZT panels in Fig. 11 rely on the QSA elastic constants, this choice should be supported by convergence tests (elastic constants vs q-mesh, smearing, volume sampling) or by comparison with any available experimental elastic data.
minor comments (5)
  1. [References] Placeholder references [126], [128], and [129] (e.g., 'A. B. Surname', 'F. N. Hyphenated-Lastname') must be replaced. The manuscript appears to contain template entries.
  2. [Fig. 11 caption] Figure 11 has panels (a)-(m), but the caption does not describe individual panels, line styles, or the exact definition of p-/n-type doping (fixed μ, fixed carrier concentration, or maximized over μ). Please expand the caption.
  3. [Abstract and §3.3] The phrase 'agreed well' is used for ZT_ML = 0.17 versus ZT_exp = 0.10 at 300 K; this is a 1.7× overestimate. The manuscript should describe this as qualitative agreement and note that the 500-K comparison (0.37 vs 0.32) is closer.
  4. [Throughout] Several typos and notation inconsistencies should be corrected: 'anab-initiomolecular dynamics', 'Thermo_PW' vs thermo_pw, 'TEDC' vs TDEC, 'Boltztrap2' vs BoltzTraP2, and inconsistent κ_l/Kl notation.
  5. [§2.2] The text says the FMLP model was trained using 'the machine learning module' but does not state which code/package (e.g., VASP ML module, NEP, GAP) was used, nor the number of training/validation structures. Please specify.

Circularity Check

0 steps flagged

No significant circularity: the MLIP is trained on DFT data, not on the target Kl/ZT, and experimental values are used only for external comparison.

full rationale

The derivation chain is self-contained. The lattice thermal conductivity is computed in two independent ways: (i) an MLIP whose training labels are DFT forces/energies from AIMD at 300 K, followed by Phonopy/Phono3py IFCs and the BTE expression in Eq. 2; and (ii) the Slack model combined with TDEC elastic constants from thermo_pw. Neither model is fitted to Kl or ZT. Electronic transport comes from HSE06/GGA/GGA+SOC band structures through BoltzTraP2 (Eqs. 6-8) plus deformation-potential relaxation times; no transport parameter is tuned to experimental ZT. The experimental values of Yang et al. enter only as a post-hoc comparison (Section 3.3: 'the theoretically obtained ZT are compared to an available experimental ZT'). The one self-citation in Section 2.2 ('Dien et al. have already validated the accuracy...') is a methodological precedent, not a load-bearing uniqueness argument; the paper independently validates the MLIP against DFPT phonon dispersion and reports DFT-vs-ML force/energy RMSEs. The QSA-over-QHA choice is a modeling decision based on the unphysical QHA C44 < 0 at ~600 K, not an equation-level reduction. The abstract's 'ZT value well above the benchmark value of 1 beyond 600 K' is not accompanied by numerical ZT values or a specified carrier concentration/chemical potential, but this is a reporting/evidence gap, not circularity.

Axiom & Free-Parameter Ledger

3 free parameters · 6 axioms · 0 invented entities

The central claim relies on standard DFT/Boltzmann transport machinery, but the MLIP transferability, the unstated doping level, and the QSA-over-QHA selection are the main non-standard inputs. No new physical entities are introduced.

free parameters (3)
  • Carrier chemical potential / doping level = Not stated; apparently selected to maximize ZT at each temperature
    The ZT>1 result requires specifying the carrier concentration; without it, the claim is an optimization envelope, not a material property. The text never reports the mu or doping level used.
  • Deformation potential constant E_d = Not reported numerically; derived from band-edge shift under uniaxial strain (Fig. S4)
    Enters eq. 10 for relaxation time; controls absolute sigma and ZT. A fitted/computed value with no uncertainty.
  • MLIP training weights (energy, forces, stresses) = 1, 0.1, 0.001
    Chosen by hand in Section 2.2; no sensitivity analysis of these weights on Kl.
axioms (6)
  • domain assumption HSE06 hybrid functional accurately describes the band gap and band dispersion of LiCdSb for transport calculations.
    In Section 2.1 and 3.1, HSE06 is used because it is 'widely known for its high accuracy'; no experimental band-gap comparison is provided.
  • domain assumption The semi-classical Boltzmann transport equation with CRTA/RTA and deformation-potential scattering captures the thermoelectric transport coefficients.
    Section 3.3 uses BoltzTraP2 and DP theory (eq. 10) without benchmarking against measured transport.
  • ad hoc to paper The MLIP trained on 10 ps AIMD at 300 K is transferable to 300-900 K and yields accurate second- and third-order IFCs.
    Section 2.2: training only at 300 K; no validation of Kl against DFT at other temperatures.
  • ad hoc to paper QHA results are discarded in favor of QSA because QHA predicts C44<0 at ~600 K, which is deemed physically unreasonable.
    Section 3.2: the authors select QSA because it gives monotonic softening; this post-hoc model choice affects Slack+TDEC Kl.
  • domain assumption The Slack equation with temperature-dependent elastic constants (TDEC/QSA) provides a reliable estimate of lattice thermal conductivity.
    Section 3.3, eq. 9; standard model but its accuracy for this material is not independently verified.
  • domain assumption The 4x4x4 supercell and 30x30x30 q-mesh give converged phonon properties.
    Section 2.2; no convergence test is reported.

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read the original abstract

By using first-principles density functional theory (DFT) and the Boltzmann transport equation, we have calculated the corresponding electronic and thermoelectric properties of LiCdSb. For calculating electron transport properties, accurate band-structure estimation is crucial. Hence, for the precise band gap calculation, we have implemented a hybrid functional HSE06, which is widely known for its high accuracy. To evaluate the thermoelectric performance of a material, the calculation of lattice thermal conductivity (Kl) is a key parameter. However, from a theoretical perspective, the calculation of lattice thermal conductivity is very complex and demands huge computational resources. Therefore, in this work, we have opted for an alternative method of machine-learning interatomic potentials (MLIPs) for the calculation of Kl. Our result of Kl=0.24 Wm^-1K^-1 at room temperature is in qualitative agreement with the available theoretical and experimental data. The figure of merit (ZT) with Kl estimated from Slack+TDEC ZT is 0.18 at 300 K, and machine learning (ML) models ZT is 0.17 at 300K, combining with HSE06-based electronic transport properties agreed well with the available experimentally reported value of ZT is 0.10 at 300K. However, we report the ZT value well above the benchmark value of 1 beyond 600K. The ZT value exceeding 1 at higher temperatures makes LiCdSb a promising material for high-temperature energy conversion.

Figures

Figures reproduced from arXiv: 2607.23203 by A. Laref, D. P. Rai, Lalhriat Zuala, N. T. Tien, R. Zosiamliana, Vo Khuong Dien.

Figure 1
Figure 1. Figure 1: (a) Optimized structure of β-phase LCS, where red, green, and blue spheres represent Li, Cd, and Sb atoms, (b) Energy variation vs lattice constant of α-, β-, and γ-phases using Birch-Murnaghan curve fitting method, and (c) Calculated phonon dispersion curve for β-phase LCS HH compound. Structural optimization shows that the LCS HH compound crystallizes in the cubic C1b-type structure with space group F-43… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Forces and (b) Energy calculated from DFT and ML. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Calculated electronic band structure using three different formalism: (a) GGA, (b) GGA+SOC, and [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Calculated total density of state (TDOS) and partial density of state (PDOS) using: (a) GGA, (b) [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Calculated TDEC (Cij (T)) based on QSA and QHA approach. 33.0 34.0 35.0 B (GPa) 52.5 54.0 55.5 Y (GPa) 21.0 21.7 22.4 G (GPa) 0.231 0.238 0.245 ν 0 200 400 600 800 T (K) 2.10 2.80 3.50 V (km s-1 ) 0 200 400 600 800 T (K) 5.13 5.16 5.19 ρ (g cm-3 ) (a) (b) (c) (d) Voigt Reuss Hill VP VB VG (e) (f) [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Calculated temperature-dependent elastic moduli (TDEM) and other parameters based on the QSA [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Calculated temperature-dependent thermodynamic properties (TDTP) using QSA and QHA models. [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Calculated temperature-dependent bulk modulus (TDBM) using QSA and QHA models. Here, B [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Variation of power factor (PF) with respect to temperature for p-type and n-type doping calculated [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The lattice thermal conductivity (Kl) calculated from Slack model, Slack model+TDEC and ML. VB edge suppresses the transport performance under p-type doping, yielding PF0(GGA) > PF0(GGA+SOC). Due to this reason, the electronic thermal conductivity (κe) which satisfies the Wiedemann-Franz relation [109] i.e., κe=LσT, where L is the Lorenz number, follows the electrical conductivity (σ) trend. Consequently,… view at source ↗
Figure 11
Figure 11. Figure 11: Thermoelectric figure of merit (ZT) from CRTA and RTA using K [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗

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Reference graph

Works this paper leans on

129 extracted references · 81 canonical work pages

  1. [1]

    Rehman, M

    A. Rehman, M. Radulescu, L. M. Cismas,, C.-M. Cismas,, A. A. Chandio, S. T. Simoni, Renewable energy, urbanization, fossil fuel consumption, and economic growth dilemma in romania: Examining the short- and long-term impact, Energies 15 (19) (2022).doi: 10.3390/en15197180. URLhttps://www.mdpi.com/1996-1073/15/19/7180

    work page doi:10.3390/en15197180 2022

  2. [2]

    Y. Xu, F. Zhao, Impact of energy depletion, human development, and income distribution on natural resource sustainability, Resources Policy 83 (2023) 103531.doi:https://doi. org/10.1016/j.resourpol.2023.103531. URLhttps://www.sciencedirect.com/science/article/pii/S0301420723002428

    arXiv 2023

  3. [3]

    F. Perera, Pollution from fossil-fuel combustion is the leading environmental threat to globalpediatric healthandequity: Solutionsexist, InternationalJournalofEnvironmental Research and Public Health 15 (1) (2018).doi:10.3390/ijerph15010016. URLhttps://www.mdpi.com/1660-4601/15/1/16

    work page doi:10.3390/ijerph15010016 2018

  4. [4]

    Mamur, Ömer Faruk Dilmaç, J

    H. Mamur, Ömer Faruk Dilmaç, J. Begum, M. R. A. Bhuiyan, Thermoelectric generators act as renewable energy sources, Cleaner Materials 2 (2021) 100030.doi:https://doi. org/10.1016/j.clema.2021.100030. URLhttps://www.sciencedirect.com/science/article/pii/S2772397621000307

    arXiv 2021

  5. [5]

    M. Feng, S. Lv, J. Deng, Y. Guo, Y. Wu, G. Shi, M. Zhang, An overview of environmental energy harvesting by thermoelectric generators, Renewable and Sustainable Energy Re- views 187 (2023) 113723.doi:https://doi.org/10.1016/j.rser.2023.113723. URLhttps://www.sciencedirect.com/science/article/pii/S1364032123005804

    arXiv 2023

  6. [6]

    Aridi, J

    R. Aridi, J. Faraj, S. Ali, T. Lemenand, M. Khaled, Thermoelectric power generators: State-of-the-art, heat recovery method, and challenges, Electricity 2 (3) (2021) 359–386. doi:10.3390/electricity2030022. URLhttps://www.mdpi.com/2673-4826/2/3/22

    work page doi:10.3390/electricity2030022 2021

  7. [7]

    Huang, L

    Y. Huang, L. Peng, S. Lin, S. Qi, H. Lv, N. V. Toan, Y. Xia, C. Xia, Z. Wang, Optimized design and performance evaluation of a flexible thermoelectric generator for low-thermal heat waste energy harvesting, Applied Thermal Engineering 264 (2025) 125225.doi: https://doi.org/10.1016/j.applthermaleng.2024.125225. URLhttps://www.sciencedirect.com/science/arti...

    arXiv 2025

  8. [8]

    Ioffe, L

    A. Ioffe, L. Stil’bans, E. Iordanishvili, T. Stavitskaya, A. V. Gelbtuch, Semiconductor thermoelements and thermoelectric cooling, Phys Today 12 (1959) 42.doi:10.1063/1. 3060810

    doi:10.1063/1 1959

  9. [9]

    W. Liu, J. Hu, S. Zhang, M. Deng, C.-G. Han, Y. Liu, New trends, strategies and op- portunities in thermoelectric materials: A perspective, Materials Today Physics 1 (2017) 50–60.doi:https://doi.org/10.1016/j.mtphys.2017.06.001. URLhttps://www.sciencedirect.com/science/article/pii/S2542529317301062

    work page doi:10.1016/j.mtphys.2017.06.001 2017

  10. [10]

    J. Yang, H. Li, T. Wu, W. Zhang, L. Chen, J. Yang, Evaluation of half-heusler compounds as thermoelectric materials based on the calculated electrical trans- port properties, Advanced Functional Materials 18 (19) (2008) 2880–2888.arXiv: https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/adfm.200701369, doi:https://doi.org/10.1002/adfm.200701369. URL...

    work page doi:10.1002/adfm.200701369 2008

  11. [11]

    W. Li, S. Ghosh, N. Liu, B. Poudel, Half-Heusler thermoelectrics: Advances from materials fundamental to device engineering, Joule 8 (5) (2024) 1274–1311. doi:10.1016/j.joule.2024.03.016. URLhttps://www.sciencedirect.com/science/article/pii/ S254243512400151Xhttps://linkinghub.elsevier.com/retrieve/pii/ S254243512400151X

    work page doi:10.1016/j.joule.2024.03.016 2024

  12. [12]

    Çorbacı, Y

    G. Çorbacı, Y. O. Ciftci, First-principles study on half-heusler semiconductor lisib com- pound, Materials Chemistry and Physics 356 (2026) 132121.doi:https://doi.org/10. 1016/j.matchemphys.2026.132121. URLhttps://www.sciencedirect.com/science/article/pii/S0254058426001124

    arXiv 2026

  13. [13]

    A. Roy, J. W. Bennett, K. M. Rabe, D. Vanderbilt, Half-heusler semiconductors as piezo- electrics, Phys. Rev. Lett. 109 (2012) 037602.doi:10.1103/PhysRevLett.109.037602. URLhttps://link.aps.org/doi/10.1103/PhysRevLett.109.037602

    work page doi:10.1103/physrevlett.109.037602 2012

  14. [14]

    Dubey, J

    S. Dubey, J. A. Abraham, K. Dubey, V. Sahu, A. Modi, G. Pagare, N. Gaur, Dft study of rhtip half heusler semiconductor: Revealing its mechanical, optoelectronic, and ther- moelectric properties, Physica B: Condensed Matter 672 (2024) 415452.doi:https: //doi.org/10.1016/j.physb.2023.415452. URLhttps://www.sciencedirect.com/science/article/pii/S0921452623008190

    arXiv 2024

  15. [15]

    Huang, F

    Y. Huang, F. Lv, S. Han, M. Chen, Y. Wang, Q. Lou, C. Fu, Y. Huang, D. Wu, F. Li, T. Zhu, Piezoelectricity in half-heusler narrow-bandgap semiconductors, Science 387 (6739) (2025) 1187–1192.arXiv:https://www.science.org/doi/pdf/10.1126/ science.ads9584,doi:10.1126/science.ads9584. URLhttps://www.science.org/doi/abs/10.1126/science.ads9584

    work page doi:10.1126/science.ads9584 2025

  16. [16]

    J. Chen, G. Gao, K. Yao, M. Song, Half-metallic ferromagnetism in the half-heusler compounds gekca and snkca from first-principles calculations, Journal of Alloys and Com- pounds 509 (42) (2011) 10172–10178.doi:https://doi.org/10.1016/j.jallcom.2011. 19 08.083. URLhttps://www.sciencedirect.com/science/article/pii/S0925838811017701

    work page doi:10.1016/j.jallcom.2011 2011

  17. [17]

    R. L. Zhang, L. Damewood, C. Y. Fong, L. H. Yang, R. W. Peng, C. Felser, A half- metallic half-heusler alloy having the largest atomic-like magnetic moment at optimized lattice constant, AIP Advances 6 (11) (2016) 115209.arXiv:https://pubs.aip.org/ aip/adv/article-pdf/doi/10.1063/1.4967365/12929618/115209_1_online.pdf, doi:10.1063/1.4967365. URLhttps://do...

    work page doi:10.1063/1.4967365/12929618/115209_1_online.pdf 2016

  18. [18]

    Nakajima, R

    Y. Nakajima, R. Hu, K. Kirshenbaum, A. Hughes, P. Syers, X. Wang, K. Wang, R. Wang, S. R. Saha, D. Pratt, J. W. Lynn, J. Paglione, Topological <i>r</i>pdbi half-heusler semimetals: A new family of noncentrosymmetric magnetic superconductors, Science Advances 1 (5) (2015) e1500242.arXiv:https://www.science.org/doi/pdf/10.1126/ sciadv.1500242,doi:10.1126/sc...

    work page doi:10.1126/sciadv.1500242 2015

  19. [19]

    C.Shekhar, N.Kumar, V.Grinenko, S.Singh, R.Sarkar, H.Luetkens, S.-C.Wu, Y.Zhang, A. C. Komarek, E. Kampert, Y. Skourski, J. Wosnitza, W. Schnelle, A. McCollam, U. Zeitler, J. Kübler, B. Yan, H.-H. Klauss, S. S. P. Parkin, C. Felser, Anomalous hall effect in weyl semimetal half-heusler compounds rptbi (r = gd and nd), Proceedings of the National Academy of...

    work page doi:10.1073/pnas.1810842115 2018

  20. [20]

    W. Feng, D. Xiao, Y. Zhang, Y. Yao, Half-heusler topological insulators: A first-principles study with the tran-blaha modified becke-johnson density functional, Phys. Rev. B 82 (2010) 235121.doi:10.1103/PhysRevB.82.235121. URLhttps://link.aps.org/doi/10.1103/PhysRevB.82.235121

    work page doi:10.1103/physrevb.82.235121 2010

  21. [21]

    Sahni, C

    Vikram, B. Sahni, C. K. Barman, A. Alam, Accelerated discovery of new 8-electron half- heusler compounds as promising energy and topological quantum materials, The Journal of Physical Chemistry C 123 (12) (2019) 7074–7080.arXiv:https://doi.org/10.1021/ acs.jpcc.9b01737,doi:10.1021/acs.jpcc.9b01737. URLhttps://doi.org/10.1021/acs.jpcc.9b01737

    work page doi:10.1021/acs.jpcc.9b01737 2019

  22. [22]

    Galanakis, P

    I. Galanakis, P. H. Dederichs, N. Papanikolaou, Slater-pauling behavior and origin of the half-metallicity of the full-heusler alloys, Phys. Rev. B 66 (2002) 174429.doi:10.1103/ PhysRevB.66.174429. URLhttps://link.aps.org/doi/10.1103/PhysRevB.66.174429

    work page doi:10.1103/physrevb.66.174429 2002

  23. [23]

    R. A. de Groot, F. M. Mueller, P. G. v. Engen, K. H. J. Buschow, New class of materials: Half-metallic ferromagnets, Phys. Rev. Lett. 50 (1983) 2024–2027.doi: 10.1103/PhysRevLett.50.2024. URLhttps://link.aps.org/doi/10.1103/PhysRevLett.50.2024

    doi:10.1103/physrevlett.50.2024 1983

  24. [24]

    L. Feng, E. Liu, W. Zhang, W. Wang, G. Wu, First-principles investigation of half-metallic ferromagnetism of half-heusler compounds xyz, Journal of Magnetism and Magnetic Ma- 20 terials 351 (2014) 92–97.doi:https://doi.org/10.1016/j.jmmm.2013.09.054. URLhttps://www.sciencedirect.com/science/article/pii/S0304885313007130

    work page doi:10.1016/j.jmmm.2013.09.054 2014

  25. [25]

    Umamaheswari, M

    R. Umamaheswari, M. Yogeswari, G. Kalpana, Ab-initio investigation of half-metallic ferromagnetism in half-heusler compounds xyz (x=li, na, k and rb; y=mg, ca, sr and ba; z=b, al and ga), Journal of Magnetism and Magnetic Materials 350 (2014) 167–173. doi:https://doi.org/10.1016/j.jmmm.2013.09.019. URLhttps://www.sciencedirect.com/science/article/pii/S030...

    work page doi:10.1016/j.jmmm.2013.09.019 2014

  26. [26]

    Lakdja, H

    A. Lakdja, H. Rozale, A. Chahed, O. Benhelal, Ferromagnetism in the half-heusler xcsba compounds from first-principles calculations (x=c, si, and ge), Journal of Alloys and Compounds 564 (2013) 8–12.doi:https://doi.org/10.1016/j.jallcom.2013.02.026. URLhttps://www.sciencedirect.com/science/article/pii/S0925838813003241

    work page doi:10.1016/j.jallcom.2013.02.026 2013

  27. [27]

    H. Luo, Z. Zhu, G. Liu, S. Xu, G. Wu, H. Liu, J. Qu, Y. Li, Ab-initio investigation of electronic properties and magnetism of half-heusler alloys xcral (x=fe, co, ni) and nicrz (z=al, ga, in), Physica B: Condensed Matter 403 (1) (2008) 200–206.doi:https: //doi.org/10.1016/j.physb.2007.08.214. URLhttps://www.sciencedirect.com/science/article/pii/S0921452607008150

    work page doi:10.1016/j.physb.2007.08.214 2008

  28. [28]

    X. Wang, Z. Cheng, G. Liu, Largest magnetic moments in the half-heusler alloys xcrz (x = li, k, rb, cs; z = s, se, te): A first-principles study, Materials 10 (9) (2017).doi: 10.3390/ma10091078. URLhttps://www.mdpi.com/1996-1944/10/9/1078

    work page doi:10.3390/ma10091078 2017

  29. [29]

    Damewood, B

    L. Damewood, B. Busemeyer, M. Shaughnessy, C. Y. Fong, L. H. Yang, C. Felser, Stabiliz- ingandincreasingthemagneticmomentofhalf-metals: Theroleofliinhalf-heuslerLiMnz (z=N,P,Si), Phys. Rev. B 91 (2015) 064409.doi:10.1103/PhysRevB.91.064409. URLhttps://link.aps.org/doi/10.1103/PhysRevB.91.064409

    work page doi:10.1103/physrevb.91.064409 2015

  30. [30]

    Kumar, S

    A. Kumar, S. L. Gupta, S. Kumar, Anupam, Diwaker, First principles analysis of novel half heusler alloys vpdz (z = ge, sn) for thermodynamic, spintronics and optoelectronic applications, Materials Chemistry and Physics 340 (2025) 130770.doi:https://doi. org/10.1016/j.matchemphys.2025.130770. URLhttps://www.sciencedirect.com/science/article/pii/S025405842500416X

    arXiv 2025

  31. [31]

    Joshi, D

    H. Joshi, D. P. Rai, A. Laref, R. K. Thapa, Electronic, and thermoelectric properties of half-heusler compounds mcosb (m = ti, zr, hf): a first principles study, Materials Research Express 6 (6) (2019) 066307.doi:10.1088/2053-1591/ab0c68. URLhttps://doi.org/10.1088/2053-1591/ab0c68

    work page doi:10.1088/2053-1591/ab0c68 2019

  32. [32]

    M. K. Yadav, B. Sanyal, First principles study of thermoelectric properties of li-based half-heusler alloys, Journal of Alloys and Compounds 622 (2015) 388–393.doi:https: //doi.org/10.1016/j.jallcom.2014.10.025. URLhttps://www.sciencedirect.com/science/article/pii/S0925838814024451

    work page doi:10.1016/j.jallcom.2014.10.025 2015

  33. [33]

    H. J. Goldsmid, Introduction to Thermoelectricity, Vol. 121 of Springer Series in Materials Science, Springer, Berlin, Heidelberg, 2010.doi:10.1007/978-3-642-00716-3. 21

    work page doi:10.1007/978-3-642-00716-3 2010

  34. [34]

    Pin-Wen, I

    Z. Pin-Wen, I. Yoshio, I. Yukihiro, S. Yoshikazi, J. Xiao-Peng, Z. Guang-Tian, High thermoelectric properties of pbte doped with bi2te3 and sb2te3, Chinese Physics Letters 22 (8) (2005) 2103.doi:10.1088/0256-307X/22/8/077. URLhttps://doi.org/10.1088/0256-307X/22/8/077

    work page doi:10.1088/0256-307x/22/8/077 2005

  35. [35]

    X. Wang, H. Shang, H. Gu, Y. Chen, Z. Zhang, Q. Zou, L. Zhang, C. Feng, G. Li, F. Ding, High-performance p-type bi2te3-based thermoelectric materials enabled via regu- lating bi–te ratio, ACS Applied Materials & Interfaces 16 (9) (2024) 11678–11685, pMID: 38386610.arXiv:https://doi.org/10.1021/acsami.3c18595,doi:10.1021/acsami. 3c18595. URLhttps://doi.org...

    work page doi:10.1021/acsami.3c18595 2024

  36. [36]

    F. Hao, P. Qiu, Y. Tang, S. Bai, T. Xing, H.-S. Chu, Q. Zhang, P. Lu, T. Zhang, D. Ren, J. Chen, X. Shi, L. Chen, High efficiency bi2te3-based materials and devices for thermo- electricpowergenerationbetween100and300°c, EnergyEnviron.Sci.9(2016)3120–3127. doi:10.1039/C6EE02017H. URLhttp://dx.doi.org/10.1039/C6EE02017H

    work page doi:10.1039/c6ee02017h 2016

  37. [37]

    J. C. Caylor, K. Coonley, J. Stuart, T. Colpitts, R. Venkatasubramanian, Enhanced thermoelectric performance in pbte-based superlattice structures from reduction of lattice thermal conductivity, Applied Physics Letters 87 (2) (2005) 023105. arXiv:https://pubs.aip.org/aip/apl/article-pdf/doi/10.1063/1.1992662/ 14644468/023105_1_online.pdf,doi:10.1063/1.199...

    work page doi:10.1063/1.1992662/ 2005

  38. [38]

    Beyer, J

    H. Beyer, J. Nurnus, H. Böttner, A. Lambrecht, E. Wagner, G. Bauer, High thermoelectric figure of merit zt in pbte and bi2te3-based superlattices by a reduction of the thermal conductivity, Physica E: Low-dimensional Systems and Nanostructures 13 (2) (2002) 965– 968.doi:https://doi.org/10.1016/S1386-9477(02)00246-1. URLhttps://www.sciencedirect.com/scienc...

    work page doi:10.1016/s1386-9477(02)00246-1 2002

  39. [39]

    K. Kaur, R. Kumar, D. Rai, A promising thermoelectric response of hfrhsb half heusler compound at high temperature: A first principle study, Journal of Alloys and Compounds 763 (2018) 1018–1023.doi:https://doi.org/10.1016/j.jallcom.2018.06.034. URLhttps://www.sciencedirect.com/science/article/pii/S0925838818321443

    work page doi:10.1016/j.jallcom.2018.06.034 2018

  40. [40]

    K. Kaur, D. P. Rai, R. K. Thapa, S. Srivastava, Structural, electronic, mechanical, and thermoelectric properties of a novel half heusler compound hfptpb, Journal of Applied Physics 122 (4) (2017) 045110.arXiv:https://pubs.aip.org/aip/jap/article-pdf/ doi/10.1063/1.4996648/15198549/045110_1_online.pdf,doi:10.1063/1.4996648. URLhttps://doi.org/10.1063/1.4996648

    work page doi:10.1063/1.4996648/15198549/045110_1_online.pdf 2017

  41. [41]

    Guo, Thermoelectric properties of half-heusler zrnipb by using first principles cal- culations, RSC Adv

    S.-D. Guo, Thermoelectric properties of half-heusler zrnipb by using first principles cal- culations, RSC Adv. 6 (2016) 47953–47958.doi:10.1039/C6RA08461C. URLhttp://dx.doi.org/10.1039/C6RA08461C

    work page doi:10.1039/c6ra08461c 2016

  42. [42]

    Anuradha, K. Kaur, R. Singh, R. Kumar, Search for thermoelectricity in li-based half- heusler alloys: a dft study, Materials Research Express 5 (1) (2018) 014009.doi:10. 22 1088/2053-1591/aaa507. URLhttps://doi.org/10.1088/2053-1591/aaa507

    work page doi:10.1088/2053-1591/aaa507 2018

  43. [43]

    J. Wei, G. Wang, Thermoelectric and optical properties of half-heusler compound tacosn: Afirst-principlestudy, JournalofAlloysandCompounds757(2018)118–123.doi:https: //doi.org/10.1016/j.jallcom.2018.05.037. URLhttps://www.sciencedirect.com/science/article/pii/S0925838818317158

    work page doi:10.1016/j.jallcom.2018.05.037 2018

  44. [44]

    S. A. Khandy, Inspecting the electronic structure and thermoelectric power factor of novel p-type half-heuslers, Sci Rep 11 (2021) 20756.doi:https://doi.org/10.1038/ s41598-021-00314-6

    2021

  45. [45]

    D. R. Jaishi, S. Bati, N. Sharma, B. Karki, B. P. Belbase, M. P. Ghimire, Rhodium- based half-heusler alloys as thermoelectric materials, Phys. Chem. Chem. Phys. 24 (2022) 19844–19852.doi:10.1039/D2CP02504C. URLhttp://dx.doi.org/10.1039/D2CP02504C

    work page doi:10.1039/d2cp02504c 2022

  46. [46]

    Zhang, N

    C. Zhang, N. Yan, C. Zhao, B. Wei, First-principles assisted design of high-entropy ther- moelectric materials based on half-heusler alloys, Journal of Applied Physics 137 (1) (2025) 015107.arXiv:https://pubs.aip.org/aip/jap/article-pdf/doi/10.1063/5. 0249228/20328708/015107_1_5.0249228.pdf,doi:10.1063/5.0249228. URLhttps://doi.org/10.1063/5.0249228

    doi:10.1063/5 2025

  47. [47]

    Ghosh, A

    S. Ghosh, A. Nozariasbmarz, H. Lee, L. Raman, S. Sharma, R. B. Smriti, D. Man- dal, Y. Zhang, S. K. Karan, N. Liu, J. L. Gray, M. Sanghadasa, Y. Xia, S. Priya, W. Li, B. Poudel, High-entropy-driven half-heusler alloys boost thermoelectric perfor- mance, Joule 8 (2024) 3303–3312.doi:doi:10.1016/j.joule.2024.08.008

    work page doi:10.1016/j.joule.2024.08.008 2024

  48. [48]

    J. Yu, C. Fu, Y. Liu, K. Xia, U. Aydemir, T. C. Chasapis, G. J. Snyder, X. Zhao, T. Zhu, Unique role of refractory ta alloying in enhancing the figure of merit of nbfesb thermoelectric materials, Advanced Energy Materials 8 (1) (2018) 1701313.arXiv: https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/aenm.201701313, doi:https://doi.org/10.1002/aenm.2...

    work page doi:10.1002/aenm.201701313 2018

  49. [49]

    X. Li, P. Yang, Y. Wang, Z. Zhang, D. Qin, W. Xue, C. Chen, Y. Huang, X. Xie, X. Wang, M. Yang, C. Wang, F. Cao, J. Sui, X. Liu, Q. Zhang, Phase boundary mapping in zrnisn half-heusler for enhanced thermoelectric performance, Research 2020 (2020).arXiv:https://spj.science.org/doi/pdf/10.34133/2020/4630948,doi:10. 34133/2020/4630948. URLhttps://spj.science...

    work page doi:10.34133/2020/4630948 2020

  50. [50]

    H. Zhu, W. Li, A. Nozariasbmarz, L. Na, Z. Yu, P. Shashank, P. Bed, Half-heusler alloys as emerging high power density thermoelectric cooling materials, Nat. Commun. 14 (2023) 3300.doi:https://doi.org/10.1038/s41467-023-38446-0

    work page doi:10.1038/s41467-023-38446-0 2023

  51. [51]

    D. P. Rai, A. Shankar, Sandeep, M. P. Ghimire, R. Khenata, R. K. Thapa, Study of the enhanced electronic and thermoelectric (te) properties of zrxhf1−x−ytaynisn: a first 23 principles study, RSC Adv. 5 (2015) 95353–95359.doi:10.1039/C5RA12897H. URLhttp://dx.doi.org/10.1039/C5RA12897H

    work page doi:10.1039/c5ra12897h 2015

  52. [52]

    Huang, R

    J. Huang, R. Liu, Q. Ma, Z. Jiang, Y. Jiang, Y. Li, C. Wang, Discovery of ybnisb-based half-heusler alloys as promising thermoelectric materials, ACS Applied Energy Materials 5 (10) (2022) 12630–12639.arXiv:https://doi.org/10.1021/acsaem.2c02269,doi: 10.1021/acsaem.2c02269. URLhttps://doi.org/10.1021/acsaem.2c02269

    work page doi:10.1021/acsaem.2c02269 2022

  53. [53]

    Hangtian, M

    Z. Hangtian, M. Jun, L. Yuwei, S. Jifeng, W. Yumei, Z. Qing, L. Guannan, S. Qichen, Z. Jiawei, F. Yuhao, H. Ran, T. Tian, L. Zihang, R. Wuyang, Y. Li, W. Zhiming, L. Jun, S. Andrei, B. Jiming, N. Kornelius, C. Gang, S. David J., R. Zhifeng, Discovery of tafesb- based half-heuslers with high thermoelectric performance, Nat. Commun. 10 (2019) 270. doi:https...

    work page doi:10.1038/s41467-018-08223-5 2019

  54. [54]

    Mitra, A

    M. Mitra, A. Benton, M. S. Akhanda, J. Qi, M. Zebarjadi, D. J. Singh, S. J. Poon, Conven- tional half-heusler alloys advance state-of-the-art thermoelectric properties, Materials To- day Physics 28 (2022) 100900.doi:https://doi.org/10.1016/j.mtphys.2022.100900. URLhttps://www.sciencedirect.com/science/article/pii/S254252932200298X

    arXiv 2022

  55. [55]

    G. Han, Y. Sun, Y. Feng, G. Lin, N. Lu, Machine learning based prediction of lattice ther- mal conductivity for half-heusler compounds using atomic information, Scientific Reports 14 (2021) 20–35.doi:10.30919/esmm5f451. URLhttp://dx.doi.org/10.30919/esmm5f451

    work page doi:10.30919/esmm5f451 2021

  56. [56]

    Miyazaki, T

    H. Miyazaki, T. Tamura, M. Mikami, K. Watanabe, N. Ide, O. M. Ozkendir, Y. Nishino, Machine learning regression guided thermoelectric materials discovery – a review, ES Materials and Manufacturing 11 (2021) 13410.doi:10.1038/s41598-021-92030-4. URLhttps://doi.org/10.1038/s41598-021-92030-4

    work page doi:10.1038/s41598-021-92030-4 2021

  57. [57]

    Athar, P

    S. Athar, P. Jund, Beyond predicted zt: Machine learning strategies for the experimental discoveryofthermoelectricmaterials, ArtificialIntelligenceChemistry4(1)(2026)100113. doi:https://doi.org/10.1016/j.aichem.2026.100113. URLhttps://www.sciencedirect.com/science/article/pii/S2949747726000072

    arXiv 2026

  58. [58]

    Florenciano, S

    I. Florenciano, S. Tortosa-Martinez, F. Molina-Lopez, Machine learning-driven optimiza- tion of thermoelectric materials laser-printed on flexible substrate, Advanced Materials Technologies n/a (n/a) e02634.arXiv:https://advanced.onlinelibrary.wiley.com/ doi/pdf/10.1002/admt.202502634,doi:https://doi.org/10.1002/admt.202502634. URLhttps://advanced.onlinel...

    work page doi:10.1002/admt.202502634

  59. [59]

    Y. Sun, X. Chen, J. Gao, W. Zhu, M. Pan, Searching for high-performance thermoelectric materials via an advanced machine learning framework, Cell Reports Physical Science 7 (2026) 2666–3864.doi:10.1016/j.xcrp.2025.103093. URLhttps://doi.org/10.1016/j.xcrp.2025.103093 24

    arXiv 2026

  60. [60]

    Zhang, X

    X. Zhang, X. Wang, W. Wang, Z. Yuan, J. Peng, J. Shi, P. He, Y. Chang, Machine learn- ing–driven thermoelectric materials: Review on prediction, optimization, and discovery, Journal of Alloys and Compounds 1050 (2026) 185711.doi:https://doi.org/10.1016/ j.jallcom.2025.185711. URLhttps://www.sciencedirect.com/science/article/pii/S0925838825072755

    arXiv 2026

  61. [61]

    M. T. Dylla, A. Dunn, S. Anand, A. Jain, G. J. Snyder, Machine learning chemi- cal guidelines for engineering electronic structures in half-heusler thermoelectric materi- als, Research 2020 (2020).arXiv:https://spj.science.org/doi/pdf/10.34133/2020/ 6375171,doi:10.34133/2020/6375171. URLhttps://spj.science.org/doi/abs/10.34133/2020/6375171

    doi:10.34133/2020/ 2020

  62. [62]

    T. Zhu, R. He, S. Gong, T. Xie, P. Gorai, K. Nielsch, J. C. Grossman, Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics, Energy Environ. Sci. 14 (2021) 3559–3566.doi:10.1039/D1EE00442E. URLhttp://dx.doi.org/10.1039/D1EE00442E

    work page doi:10.1039/d1ee00442e 2021

  63. [63]

    Tranås, O

    R. Tranås, O. M. Løvvik, O. Tomic, K. Berland, Lattice thermal conductivity of half- heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis, Computational Materials Science 202 (2022) 110938.doi:https://doi.org/10.1016/j.commatsci.2021.110938. URLhttps://www.sciencedirect.c...

    arXiv 2022

  64. [64]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al., Scikit-learn: Machine learning in python, Journal of Machine Learning Research 12 (2011) 2825–2830

    2011

  65. [65]

    Legrain, J

    F. Legrain, J. Carrete, A. van Roekeghem, G. K. Madsen, N. Mingo, Materials screening for the discovery of new half-heuslers: Machine learning versus ab initio methods, The Journal of Physical Chemistry B 122 (2) (2018) 625–632, pMID: 28742351.arXiv:https: //doi.org/10.1021/acs.jpcb.7b05296,doi:10.1021/acs.jpcb.7b05296. URLhttps://doi.org/10.1021/acs.jpcb.7b05296

    work page doi:10.1021/acs.jpcb.7b05296 2018

  66. [66]

    X. Jia, Y. Deng, X. Bao, H. Yao, S. Li, Z. Li, C. Chen, X. Wang, J. Mao, F. Cao, J. Sui, J. Wu, C. Wang, Q. Zhang, X. Liu, Unsupervised machine learning for discovery of promising half-heusler thermoelectric materials, npj Computational Materials 8 (2022) 34

    2022

  67. [67]

    Filanovich, A

    A. Filanovich, A. Povzner, A. Lukoyanov, Machine learning prediction of thermal and elasticpropertiesofdoublehalf-heusleralloys, MaterialsChemistryandPhysics306(2023) 128030.doi:https://doi.org/10.1016/j.matchemphys.2023.128030. URLhttps://www.sciencedirect.com/science/article/pii/S0254058423007381

    arXiv 2023

  68. [68]

    Y. Yang, Y. Lin, S. Dai, Y. Zhu, J. Xi, L. Xi, X. Gu, D. J. Singh, W. Zhang, J. Yang, Hh130: a standardized database of machine learning interatomic potentials, datasets, and its applications in the thermal transport of half-heusler thermoelectrics, Digital Discovery 3 (2024) 2201–2210.doi:10.1039/D4DD00240G. URLhttp://dx.doi.org/10.1039/D4DD00240G 25

    work page doi:10.1039/d4dd00240g 2024

  69. [69]

    A. Ojha, A. Tiwari, S. A. T. Vambaravelli, K. K. Sahu, S. Bathula, Exploring phase stability and transport properties of emerging thermoelectric materials: Machine learning and experimental insights, ACS Applied Energy Materials 8 (15) (2025) 11270–11283. arXiv:https://doi.org/10.1021/acsaem.5c01456,doi:10.1021/acsaem.5c01456. URLhttps://doi.org/10.1021/a...

    work page doi:10.1021/acsaem.5c01456 2025

  70. [70]

    V. K. Elavunkel, P. Padhan, Unlocking thermoelectric potential: A machine learn- ing stacking approach for half-heusler alloys, ACS Applied Energy Materials 8 (20) (2025) 15241–15257.arXiv:https://doi.org/10.1021/acsaem.5c02223,doi:10. 1021/acsaem.5c02223. URLhttps://doi.org/10.1021/acsaem.5c02223

    work page doi:10.1021/acsaem.5c02223 2025

  71. [71]

    Fronzi, M

    M. Fronzi, M. J. Ford, K. S. Nayal, O. Isayev, C. Stampfl, Interpretable machine learning for thermoelectric materials design with kolmogorov–arnold networks, Scientific Reports 16 (2026) 14146.doi:10.1038/s41598-026-44723-x

    doi:10.1038/s41598-026-44723-x 2026

  72. [72]

    Thanh Tien, P

    N. Thanh Tien, P. T. Bich Thao, D. Khanh Nguyen, L. Nhat Thanh, V. Khuong Dien, Thermoelectric properties of penta-inp5: A first-principles and machine learning study, Journal of Applied Physics 137 (8) (2025) 084302.arXiv:https://pubs.aip.org/ aip/jap/article-pdf/doi/10.1063/5.0251741/20415527/084302_1_5.0251741.pdf, doi:10.1063/5.0251741. URLhttps://doi...

    work page doi:10.1063/5.0251741/20415527/084302_1_5.0251741.pdf 2025

  73. [73]

    Hafner, Ab-initio simulations of materials using VASP: Density-functional theory and beyond, Journal of Computational Chemistry 29 (13) (2008) 2044–2078.doi:10.1002/ jcc.21057

    J. Hafner, Ab-initio simulations of materials using VASP: Density-functional theory and beyond, Journal of Computational Chemistry 29 (13) (2008) 2044–2078.doi:10.1002/ jcc.21057. URL/doi/pdf/10.1002/jcc.21057https://onlinelibrary.wiley.com/doi/abs/10. 1002/jcc.21057https://onlinelibrary.wiley.com/doi/10.1002/jcc.21057

    work page doi:10.1002/jcc.21057https://onlinelibrary.wiley.com/doi/abs/10 2008

  74. [74]

    Kresse, J

    G. Kresse, J. Furthmüller, Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set, Physical Review B 54 (16) (1996) 11169–11186.doi:10. 1103/PhysRevB.54.11169. URLhttps://journals.aps.org/prb/abstract/10.1103/PhysRevB.54.11169https: //link.aps.org/doi/10.1103/PhysRevB.54.11169

    work page doi:10.1103/physrevb.54.11169https: 1996

  75. [75]

    J. P. Perdew, K. Burke, M. Ernzerhof, Generalized Gradient Approximation Made Simple, Physical Review Letters 77 (18) (1996) 3865–3868.doi:10.1103/PhysRevLett.77.3865. URLhttps://journals.aps.org/prl/abstract/10.1103/PhysRevLett.77. 3865https://link.aps.org/doi/10.1103/PhysRevLett.77.3865

    doi:10.1103/physrevlett.77.3865 1996

  76. [76]

    F. D. Murnaghan, The Compressibility of Media under Extreme Pressures, Proceedings of the National Academy of Sciences 30 (9) (1944) 244–247.doi:10.1073/pnas.30.9.244. URL/doi/pdf/10.1073/pnas.30.9.244?download=truehttps://pnas.org/doi/ full/10.1073/pnas.30.9.244

    doi:10.1073/pnas.30.9.244 1944

  77. [77]

    J. L. Nazareth, Conjugate gradient method, WIREs Computational Statistics 1 (3) (2009) 348–353.doi:10.1002/wics.13. URL/doi/pdf/10.1002/wics.13https://onlinelibrary.wiley.com/doi/abs/10. 1002/wics.13https://wires.onlinelibrary.wiley.com/doi/10.1002/wics.13 26

    work page doi:10.1002/wics.13 2009

  78. [78]

    H. J. Monkhorst, J. D. Pack, Special points for Brillouin-zone integrations, Physical Re- view B 13 (12) (1976) 5188–5192.doi:10.1103/PhysRevB.13.5188. URLhttps://journals.aps.org/prb/abstract/10.1103/PhysRevB.13.5188https: //link.aps.org/doi/10.1103/PhysRevB.13.5188

    doi:10.1103/physrevb.13.5188 1976

  79. [79]

    J. Heyd, G. E. Scuseria, M. Ernzerhof, Hybrid functionals based on a screened Coulomb potential, The Journal of Chemical Physics 118 (18) (2003) 8207–8215. doi:10.1063/1.1564060. URL/aip/jcp/article/118/18/8207/460359/Hybrid-functionals-based-on-a-screened-Coulombhttps: //pubs.aip.org/jcp/article/118/18/8207/460359/Hybrid-functionals-based-on-a-screened-Coulomb

    doi:10.1063/1.1564060 2003

  80. [80]

    Giannozzi, O

    P. Giannozzi, O. Baseggio, P. Bonfà, D. Brunato, R. Car, I. Carnimeo, C. Cavazzoni, S. de Gironcoli, P. Delugas, F. Ferrari Ruffino, A. Ferretti, N. Marzari, I. Timrov, A. Urru, S. Baroni, Q <scp>uantum</scp> ESPRESSO toward the exascale, The Journal of Chemical Physics 152 (15) (apr 2020).arXiv:2104.10502,doi:10.1063/5.0005082. URL/aip/jcp/article/152/15...

    Pith/arXiv arXiv 2020

Showing first 80 references.