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Title: Machine learning maximized Anderson localization of phonons in aperiodic superlattices

Abstract

Nanostructuring materials to achieve ultra-low lattice thermal conductivity has proven to be extremely attractive for numerous applications such as thermoelectric energy conversion. Anderson localization of phonons due to aperiodicity can reduce thermal conductivity in superlattices, but the lower limit of thermal conductivity remains elusive due to the prohibitively large design space. In this work, we demonstrate that an intuition-based manual search for aperiodic superlattice structures (random multilayers or RMLs) with the lowest thermal conductivity yields only a local minimum, while a genetic algorithm (GA) based approach can efficiently identify the globally minimum thermal conductivity by only exploring a small fraction of the design space. Our results show that this minimum value occurs at an average RML period that is, surprisingly, smaller than the period corresponding to the minimum SL thermal conductivity. Interestingly, above this critical period, scattering of incoherent phonons at interfaces is less, whereas below this period, the room for randomization becomes less, thus putting more coherent phonons out of Anderson localization and causing increased thermal conductivity. Moreover, the lower limit of the thermal conductivity occurs at a moderate rather than maximum randomness of the layer thickness. Our machine learning approach demonstrates a general process of exploring an otherwisemore » prohibitively large design space to gain non-intuitive physical insights.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [1]; ORCiD logo [3];  [2];  [1]
  1. Purdue Univ., West Lafayette, IN (United States)
  2. Lockheed Martin Advanced Technology Lab., Cherry Hill, NJ (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Defense Advanced Research Projects Agency (DARPA); Purdue University; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
OSTI Identifier:
1606734
Alternate Identifier(s):
OSTI ID: 1581826
Grant/Contract Number:  
AC05-00OR22725; HR0011-15-2-0037
Resource Type:
Accepted Manuscript
Journal Name:
Nano Energy
Additional Journal Information:
Journal Volume: 69; Journal Issue: C; Journal ID: ISSN 2211-2855
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Random multilayer; Anderson localization; Thermal conductivity; Machine learning; Molecular dynamics

Citation Formats

Roy Chowdhury, Prabudhya, Reynolds, Colleen, Garrett, Adam, Feng, Tianli, Adiga, Shashishekar P., and Ruan, Xiulin. Machine learning maximized Anderson localization of phonons in aperiodic superlattices. United States: N. p., 2019. Web. https://doi.org/10.1016/j.nanoen.2019.104428.
Roy Chowdhury, Prabudhya, Reynolds, Colleen, Garrett, Adam, Feng, Tianli, Adiga, Shashishekar P., & Ruan, Xiulin. Machine learning maximized Anderson localization of phonons in aperiodic superlattices. United States. https://doi.org/10.1016/j.nanoen.2019.104428
Roy Chowdhury, Prabudhya, Reynolds, Colleen, Garrett, Adam, Feng, Tianli, Adiga, Shashishekar P., and Ruan, Xiulin. Sat . "Machine learning maximized Anderson localization of phonons in aperiodic superlattices". United States. https://doi.org/10.1016/j.nanoen.2019.104428. https://www.osti.gov/servlets/purl/1606734.
@article{osti_1606734,
title = {Machine learning maximized Anderson localization of phonons in aperiodic superlattices},
author = {Roy Chowdhury, Prabudhya and Reynolds, Colleen and Garrett, Adam and Feng, Tianli and Adiga, Shashishekar P. and Ruan, Xiulin},
abstractNote = {Nanostructuring materials to achieve ultra-low lattice thermal conductivity has proven to be extremely attractive for numerous applications such as thermoelectric energy conversion. Anderson localization of phonons due to aperiodicity can reduce thermal conductivity in superlattices, but the lower limit of thermal conductivity remains elusive due to the prohibitively large design space. In this work, we demonstrate that an intuition-based manual search for aperiodic superlattice structures (random multilayers or RMLs) with the lowest thermal conductivity yields only a local minimum, while a genetic algorithm (GA) based approach can efficiently identify the globally minimum thermal conductivity by only exploring a small fraction of the design space. Our results show that this minimum value occurs at an average RML period that is, surprisingly, smaller than the period corresponding to the minimum SL thermal conductivity. Interestingly, above this critical period, scattering of incoherent phonons at interfaces is less, whereas below this period, the room for randomization becomes less, thus putting more coherent phonons out of Anderson localization and causing increased thermal conductivity. Moreover, the lower limit of the thermal conductivity occurs at a moderate rather than maximum randomness of the layer thickness. Our machine learning approach demonstrates a general process of exploring an otherwise prohibitively large design space to gain non-intuitive physical insights.},
doi = {10.1016/j.nanoen.2019.104428},
journal = {Nano Energy},
number = C,
volume = 69,
place = {United States},
year = {2019},
month = {12}
}

Journal Article:

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Cited by: 2 works
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