Complex temperature dependence of coherent and incoherent lattice thermal transport in superlattices
- 1. Department of Mechanical Engineering, University of Nevada, Reno, Reno, NV 89557 (United States)
Description
Currently, it is still unclear how and to what extent a change in temperature impacts the relative contributions of coherent and incoherent phonons to thermal transport in superlattices. Some seemingly conflicting computational and experimental observations of the temperature dependence of lattice thermal conductivity make the coherent–incoherent thermal transport behaviors in superlattices even more elusive. In this work, we demonstrate that incoherent phonon contribution to thermal transport in superlattices increases as the temperature increases due to elevated inelastic interfacial transmission. On the other hand, the coherent phonon contribution decreases at higher temperatures due to elevated anharmonic scattering. The competition between these two conflicting mechanisms can lead to different trends of lattice thermal conductivity as temperature increases, i.e. increasing, decreasing, or non-monotonic. Finally, we demonstrate that the neural network-based machine learning model can well capture the coherent–incoherent transition of lattice thermal transport in the superlattice, which can greatly aid the understanding and optimization of thermal transport properties of superlattices. (paper)
Availability note (English)
Available from http://dx.doi.org/10.1088/1361-6528/abc2efAdditional details
Identifiers
Publishing Information
- Journal Title
- Nanotechnology (Print)
- Journal Volume
- 32
- Journal Issue
- 6
- Journal Page Range
- [11 p.]
- ISSN
- 0957-4484
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 53071554
- Subject category
- S75: CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; S77: NANOSCIENCE AND NANOTECHNOLOGY;
- Descriptors DEI
- MACHINE LEARNING; NEURAL NETWORKS; PHONONS; SCATTERING; SUPERLATTICES; TEMPERATURE DEPENDENCE; TEMPERATURE RANGE 0400-1000 K; THERMAL CONDUCTIVITY
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING; MATHEMATICAL LOGIC; PHYSICAL PROPERTIES; QUASI PARTICLES; TEMPERATURE RANGE; THERMODYNAMIC PROPERTIES