Machine learning maximized Anderson localization of phonons in aperiodic superlattices
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.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- Defense Advanced Research Projects Agency (DARPA); Purdue University; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- Grant/Contract Number:
- AC05-00OR22725; HR0011-15-2-0037
- OSTI ID:
- 1606734
- Alternate ID(s):
- OSTI ID: 1581826
- Journal Information:
- Nano Energy, Vol. 69, Issue C; ISSN 2211-2855
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
Prediction of Bi2Te3-Sb2Te3 Interfacial Conductance and Superlattice Thermal Conductivity Using Molecular Dynamics Simulations
Dynamical localization of electrons in an aperiodic superlattice