Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor

TL;DR

This paper presents a universal descriptor based on atomic number and sound velocity for rapid screening of crystalline materials with ultralow thermal conductivity, validated by experiments and advanced simulations.

Contribution

Introduces a universal, scalable descriptor for thermal conductivity, enabling efficient discovery of ultralow thermal conductivity materials through high-throughput screening and machine learning.

Findings

Identified materials with record-low thermal conductivity of 0.15-0.16 W/m/K.

Validated the descriptor with experimental measurements on CsAg2I3.

Linked ultralow conductivity to small sound velocity, anharmonicity, and structural complexity.

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 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.