High-throughput computational discovery of 40 ultralow thermal conductivity and 20 highly anisotropic crystalline materials
Ankit Jain, Harish P Veeravenkata, Shravan Godse, and Yagyank, Srivastava

TL;DR
This study uses high-throughput ab-initio calculations to identify 40 materials with ultralow thermal conductivity and 20 materials with highly anisotropic thermal transport, advancing the search for thermally efficient materials.
Contribution
It introduces a computational approach to discover materials with exceptional thermal properties, identifying new candidates with ultralow conductivity and high anisotropy.
Findings
40 materials with thermal conductivity below 1 W/m-K at 300 K
Six materials with anisotropy larger than 5
20 materials with higher anisotropy than previously reported
Abstract
We performed ab-initio driven density functional theory-based high throughput computations to search for materials with low thermal conductivity and high thermal transport anisotropy. We shortlisted a pool of 429 stable ternary semiconductors from the Materials Project and obtained phonon thermal conductivity by solving the Boltzmann transport equation on 225 materials. We found the lowest thermal conductivity of 0.16 W/m-K in SbRbK 2 and 40 materials with a thermal conductivity lower than 1 W/m-K at 300 K. For anisotropic thermal transport, we have identified six materials with anisotropy larger than 5 and 20 with thermal transport anisotropy higher than the largest reported literature value.
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Taxonomy
TopicsThermal properties of materials · Machine Learning in Materials Science · Advanced Thermoelectric Materials and Devices
