Deterministic inverse design of Tamm plasmon thermal emitters with multi-resonant control

2021 ◽  
Author(s):  
Mingze He ◽  
J. Ryan Nolen ◽  
Josh Nordlander ◽  
Angela Cleri ◽  
Nathaniel S. McIlwaine ◽  
...  
2021 ◽  
Author(s):  
Mingze He ◽  
J. Nolen ◽  
Josh Nordlander ◽  
Angela Cleri ◽  
Nathan Mcllwaine ◽  
...  

Abstract Wavelength-selective thermal emitters (WS-EMs) are of high interest due to the lack of cost-effective, narrow-band light sources in the mid- to long-wave infrared. Cost-effective WS-EMs can be realized via Tamm plasmon polariton (TPP) structures supported by distributed Bragg reflectors (DBRs) on metal layers, however, optimizing TPP-WS-EMs is challenging because of the large number of parameters to optimize. To address this challenge, we use stochastic gradient descent (SGD) to optimize TPP-WS-EMs composed of an aperiodic DBR deposited on doped cadmium oxide (CdO) plasmonic films. While the SGD-optimized, aperiodic DBR offers extensive spectral control, the material choice, i.e., plasma-frequency-tunable doped CdO, enables the design capabilities not accessible with noble metals. Here, the individual layer thickness and carrier density of CdO are optimized by our SGD inverse design strategy. The resultant experimental designs demonstrate TPP-WS-EMs exhibiting isolated, high-Q (narrow bandwidth), and structures featuring multiple emission bands for applications such as free-space communications and gas sensing. Furthermore, we illustrate the deterministic design capability within the infrared, such as user-designated Q-factors (28 − 10,127) at a desired frequency, multi-band emitters with user-defined Q, and the ability to directly match arbitrary chemical absorption spectra. Thus, the combination of our SGD inverse design and the broadly tunable plasma frequency of CdO enables lithography-free, CMOS-compatible, and wafer-scale solutions for WS-EMs with unprecedented spectral control.


2020 ◽  
Vol 51 (1) ◽  
pp. 1-13
Author(s):  
Anatoliy Longinovich Bolsunovsky ◽  
Nikolay Petrovich Buzoverya ◽  
Nikita Aleksandrovich Pushchin

Author(s):  
Tjoetjoek Eko PAMBAGJO ◽  
Kazuhiro NAKAHASHI ◽  
Shigeru OBAYASHI

2020 ◽  
Author(s):  
Nathaniel Park ◽  
Dmitry Yu. Zubarev ◽  
James L. Hedrick ◽  
Vivien Kiyek ◽  
Christiaan Corbet ◽  
...  

The convergence of artificial intelligence and machine learning with material science holds significant promise to rapidly accelerate development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties. Following recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity of the polymers obtained from cyclic lactones and carbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class.<br>


Author(s):  
Atefeh Kariminia ◽  
Mahdi Nili-Ahmadabadi ◽  
Kyung Chun Kim
Keyword(s):  

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