2020/04/18 by Jiahao Yan, Yan, Jiahao, Han Wei +7
Materials Science · Physics and Astronomy · #74A15 #Applied Physics (physics.app-ph) #Block Copolymer Self-Assembly #FOS: Physical sciences #Machine Learning in Materials Science #Thermal properties of materials #msc:74A15 #physics.app-ph
paper · pdf · doi:10.48550/arxiv.2004.08654
30 pages, 7 figures
arxiv created 2020/04/18 · openalex publication_date 2020/04/18 · arxiv updated 2020/04/21 · openalex created_date 2020/04/24 · openalex updated_date 2026/07/28
The emergence of data-driven science has opened up new avenues for understanding the thermophysical properties of materials. For decades, alloys are known to possess very low thermal conductivity, but the extreme thermal conductivity can be achieved by alloying has never been identified. In this work, we combine the Bayesian optimization with a high throughput thermal conductivity calculation to search for the lowest thermal conductivity atomic configuration of \rmSi0.5Ge0.5 alloy. It is found layered structures are most beneficial for reducing the thermal conductivity among all atomic configurations, which is attributed to the strong branch-folding effect. Furthermore, the roles of interface roughness and layer thicknesses in producing the lowest thermal conductivity are investigated. Through another comprehensive search using Bayesian optimization, the layered structure with smooth interfaces and optimized layer thickness arrangement is identified as the optimal structure with the lowest thermal conductivity.