论文标题
寻求低温电导率原子配置$ \ rm {si_ {0.5} ge_ {0.5}} $合金，并具有贝叶斯优化
Seeking for low thermal conductivity atomic configurations in $\rm{Si_{0.5}Ge_{0.5}}$ alloys with Bayesian Optimization
论文作者
论文摘要
数据驱动的科学的出现开辟了新的途径，以了解材料的热物理特性。几十年来，已知合金具有非常低的导热率，但是从未鉴定出通过合金来实现极端的热导率。在这项工作中，我们将贝叶斯优化与高吞吐量热导率计算结合在一起，以搜索$ \ rm {si_ {0.5} ge_ {0.5}} $合金的最低导热率原子配置。发现分层结构对于降低所有原子构型之间的热导率最有益，这归因于强的分支折叠效果。此外，研究了界面粗糙度和层厚度在产生最低导热率中的作用。通过使用贝叶斯优化的另一次全面搜索，具有光滑界面和优化层厚度排列的分层结构被确定为最低导热率的最佳结构。
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 $\rm{Si_{0.5}Ge_{0.5}}$ 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.
