Sakurai Atsushi, Yada Kyohei, Simomura Tetsushi, Ju Shenghong, Kashiwagi Makoto, Okada Hideyuki, Nagao Tadaaki, Tsuda Koji, Shiomi Junichiro
Department of Mechanical and Production Engineering, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, Japan.
National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan.
ACS Cent Sci. 2019 Feb 27;5(2):319-326. doi: 10.1021/acscentsci.8b00802. Epub 2019 Jan 22.
We computationally designed an ultranarrow-band wavelength-selective thermal radiator via a materials informatics method alternating between Bayesian optimization and thermal electromagnetic field calculation. For a given target infrared wavelength, the optimal structure was efficiently identified from over 8 billion candidates of multilayers consisting of multiple components (Si, Ge, and SiO). The resulting optimized structure is an aperiodic multilayered metamaterial exhibiting high and sharp emissivity with a Q-factor of 273. The designed metamaterials were then fabricated, and reasonable experimental realization of the optimal performance was achieved with a Q-factor of 188, which is significantly higher than those of structures empirically designed and fabricated in the past. This is the first demonstration of the experimental realization of metamaterials designed by Bayesian optimization. The results facilitate the machine-learning-based design of metamaterials and advance our understanding of the narrow-band thermal emission mechanism of aperiodic multilayered metamaterials.
我们通过一种在贝叶斯优化和热电磁场计算之间交替的材料信息学方法，对超窄带波长选择性热辐射器进行了计算设计。对于给定的目标红外波长，从由多种成分（硅、锗和二氧化硅）组成的多层结构的80多亿个候选结构中高效识别出了最优结构。所得的优化结构是一种非周期性多层超材料，具有高且尖锐的发射率，品质因数为273。然后制造了所设计的超材料，并以188的品质因数实现了最优性能的合理实验验证，这显著高于过去凭经验设计和制造的结构。这是首次展示通过贝叶斯优化设计的超材料的实验实现。这些结果促进了基于机器学习的超材料设计，并加深了我们对非周期性多层超材料窄带热发射机制的理解。
