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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.06557 |
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| _version_ | 1866915002349256704 |
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| author | Lin, Chia-Min Khatri, Abishek Yan, Da Chen, Cheng-Chien |
| author_facet | Lin, Chia-Min Khatri, Abishek Yan, Da Chen, Cheng-Chien |
| contents | We perform machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, $κ_L$. Several cadmium (Cd) compounds containing elements from the alkali-metal and carbon groups including A$_2$CdX (A = Li, Na, and K; X = Pb, Sn, and Ge) are predicted by our ML models to exhibit very low $κ_L$ values ($< 1.0 $ W/mK), rendering these materials suitable for potential thermal management and insulation applications. Further DFT calculations of electronic and transport properties indicate that the figure of merit, $ZT$, for thermoelectric performance can exceed 1.0 in compounds such as K$_2$CdPb, K$_2$CdSn, and K$_2$CdGe, which are thereby also promising thermoelectric materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning and First-Principles Predictions of Materials with Low Lattice Thermal Conductivity Lin, Chia-Min Khatri, Abishek Yan, Da Chen, Cheng-Chien Materials Science We perform machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, $κ_L$. Several cadmium (Cd) compounds containing elements from the alkali-metal and carbon groups including A$_2$CdX (A = Li, Na, and K; X = Pb, Sn, and Ge) are predicted by our ML models to exhibit very low $κ_L$ values ($< 1.0 $ W/mK), rendering these materials suitable for potential thermal management and insulation applications. Further DFT calculations of electronic and transport properties indicate that the figure of merit, $ZT$, for thermoelectric performance can exceed 1.0 in compounds such as K$_2$CdPb, K$_2$CdSn, and K$_2$CdGe, which are thereby also promising thermoelectric materials. |
| title | Machine Learning and First-Principles Predictions of Materials with Low Lattice Thermal Conductivity |
| topic | Materials Science |
| url | https://arxiv.org/abs/2408.06557 |