English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The emergence of data-driven science has opened up new avenues forunderstanding the thermophysical properties of materials. For decades, alloysare known to possess very low thermal conductivity, but the extreme thermalconductivity can be achieved by alloying has never been identified. In thiswork, we combine the Bayesian optimization with a high throughput thermalconductivity calculation to search for the lowest thermal conductivity atomicconfiguration of $\rm{Si_{0.5}Ge_{0.5}}$ alloy. It is found layered structuresare most beneficial for reducing the thermal conductivity among all atomicconfigurations, which is attributed to the strong branch-folding effect.Furthermore, the roles of interface roughness and layer thicknesses inproducing the lowest thermal conductivity are investigated. Through anothercomprehensive search using Bayesian optimization, the layered structure withsmooth interfaces and optimized layer thickness arrangement is identified asthe optimal structure with the lowest thermal conductivity.

Seeking for low thermal conductivity atomic configurations in $\rm{Si_{0.5}Ge_{0.5}}$ alloys with Bayesian Optimization