In the realm of energy conversion and efficiency, a team of researchers from various institutions, including the Chinese Academy of Sciences, the Institute of Physics, Czech Academy of Sciences, and the University of Rennes, has made a significant stride. Their work, published in the journal Nature Materials, focuses on the discovery of crystalline materials with ultralow lattice thermal conductivity, a crucial factor in enhancing energy conversion efficiency in thermoelectrics and thermal insulators.
The team, led by Xingchen Shen and including Jiongzhi Zheng, Michael Marek Koza, Petr Levinsky, Jiri Hejtmanek, Philippe Boullay, Bernard Raveau, Jinghui Wang, Jun Li, Pierric Lemoine, Christophe Candolfi, and Emmanuel Guilmeau, introduced a universal descriptor for thermal conductivity. This descriptor relies solely on the atomic number in the primitive cell and the sound velocity, enabling rapid and scalable materials screening.
The researchers coupled this descriptor with high-throughput workflows and universal machine learning potentials to screen over 25,000 materials. This approach allowed them to identify candidate materials with ultralow thermal conductivity. They further validated their method 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.
To understand the underlying mechanisms, the team combined inelastic neutron scattering with first-principles calculations. They attributed the ultralow thermal conductivity to the intrinsically small sound velocity, strong anharmonicity, and structural complexity of the material.
The practical applications of this research for the energy sector are significant. In thermoelectrics, materials with ultralow thermal conductivity can enhance the conversion of heat into electricity, improving the efficiency of waste heat recovery systems. In thermal insulators, these materials can provide better insulation, reducing heat loss in buildings and industrial processes.
This work illustrates the power of combining a universal descriptor with high-throughput screening, machine-learning potential, and experimental validation. It paves the way for the efficient discovery of materials with ultralow thermal conductivity, potentially revolutionizing the energy industry. The research was published in Nature Materials, a renowned journal in the field of materials science.
This article is based on research available at arXiv.