Accelerated discovery of crystalline materials with record ultralow lattice thermal conductivity via a universal descriptor

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 CsAg<sub>2</sub>I<sub>3</sub>. 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.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-12-17
DOI
https://doi.org/10.1038/s41467-025-67333-z
Citations
1
Primary Topic
Thermal properties of materials
Type
article
Field-Weighted Citation Impact
0.52
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accelerated discovery of crystalline materials with record ultralow lattice thermal conductivity via a universal descriptor

Petr Levinský, Michael Marek Koza, B. Raveau, Philippe Boullay et al.
1 citations
Nature Communications
Thermal properties of materials
0.52
article

Accelerated discovery of crystalline materials with record ultralow lattice thermal conductivity via a universal descriptor

Petr Levinský, Michael Marek Koza, B. Raveau, Philippe Boullay, Christophe Candolfi, Jiří Hejtmánek, Emmanuel Guilmeau, Pierric Lemoine, Jiongzhi Zheng, Xingchen Shen, Jun Li, Jinghui Wang
article en
1 citations

Abstract

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 CsAg<sub>2</sub>I<sub>3</sub>. 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.

Nature CommunicationsVol. 17(1)
Dartmouth College (US), Centre National de la Recherche Scientifique (FR), Northwestern Polytechnical University (CN), École Nationale Supérieure d'Ingénieurs de Caen (FR), ShanghaiTech University (CN), Institut Langevin (FR), Normandie Université (FR), Institut Laue-Langevin (FR), FZU ‒ Institute of Physics of the Academy of Sciences of the Czech Republic (CZ), Institut Jean Lamour (FR), Université de Lorraine (FR), Université de Caen Normandie (FR)
Openalex Percentile: Top 29%
Thermal properties of materials
0.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.