Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918258764939264 |
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| author | Shen, Xingchen Zheng, Jiongzhi Koza, Michael Marek Levinsky, Petr Hejtmanek, Jiri Boullay, Philippe Raveau, Bernard Wang, Jinghui Li, Jun Lemoine, Pierric Candolfi, Christophe Guilmeau, Emmanuel |
| author_facet | Shen, Xingchen Zheng, Jiongzhi Koza, Michael Marek Levinsky, Petr Hejtmanek, Jiri Boullay, Philippe Raveau, Bernard Wang, Jinghui Li, Jun Lemoine, Pierric Candolfi, Christophe Guilmeau, Emmanuel |
| contents | Ultralow glass-like lattice thermal conductivity in crystalline materials is crucial for enhancing energy conversion efficiency in thermoelectrics and thermal insulators. We introduce a universal descriptor for thermal conductivity that relies only on the atomic number in the primitive cell and the sound velocity, enabling fast and scalable materials screening. Coupled with high-throughput workflows and universal machine learning potentials, we identify the candidate materials with ultralow thermal conductivity from over 25, 000 materials. We further validate this approach by experimentally confirming record-low thermal conductivity values of 0.15-0.16 W/m/K from 170 to 400 K in the halide metal CsAg2I3. Combining inelastic neutron scattering with first-principles calculations, we attribute the ultralow thermal conductivity to the intrinsically small sound velocity, strong anharmonicity, and structural complexity. Our work illustrates how a universal descriptor, combined with high-throughput screening, machine-learning potential and experiment, enables the efficient discovery of materials with ultralow thermal conductivity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor Shen, Xingchen Zheng, Jiongzhi Koza, Michael Marek Levinsky, Petr Hejtmanek, Jiri Boullay, Philippe Raveau, Bernard Wang, Jinghui Li, Jun Lemoine, Pierric Candolfi, Christophe Guilmeau, Emmanuel Materials Science Ultralow glass-like lattice thermal conductivity in crystalline materials is crucial for enhancing energy conversion efficiency in thermoelectrics and thermal insulators. We introduce a universal descriptor for thermal conductivity that relies only on the atomic number in the primitive cell and the sound velocity, enabling fast and scalable materials screening. Coupled with high-throughput workflows and universal machine learning potentials, we identify the candidate materials with ultralow thermal conductivity from over 25, 000 materials. We further validate this approach by experimentally confirming record-low thermal conductivity values of 0.15-0.16 W/m/K from 170 to 400 K in the halide metal CsAg2I3. Combining inelastic neutron scattering with first-principles calculations, we attribute the ultralow thermal conductivity to the intrinsically small sound velocity, strong anharmonicity, and structural complexity. Our work illustrates how a universal descriptor, combined with high-throughput screening, machine-learning potential and experiment, enables the efficient discovery of materials with ultralow thermal conductivity. |
| title | Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor |
| topic | Materials Science |
| url | https://arxiv.org/abs/2511.21172 |