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Understanding Coherent and Incoherent Phonon Transport in Superlattices Towards Minimized Lattice Thermal Conductivity
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The rational design of nanostructured metamaterials is of fundamental interest in a wide range of applications, including thermal management and energy harvesting. Among the metamaterials, periodic and aperiodic superlattices are getting significant attention for their ultralow lattice thermal conductivity, which is essential for thermoelectrics, thermal barrier coating, heat-assisted magnetic writing, and many more. To have the best output of these superlattice structures, it is of fundamental interest to understand and strategically manipulate the coherent (wave-like) and incoherent (particle-like) phonon transport. Besides, developing novel ideas for hierarchically suppressing the coherent and incoherent phonon transport in superlattice structures is essential for minimizing the lattice thermal conductivity of superlattice structures to improve their effectiveness in applications like thermoelectrics and thermal barrier coatings. In this work, we conduct non-equilibrium molecular dynamics simulations to propose and reveal specific strategies to reduce the lattice thermal conductivity of thick periodic and aperiodic superlattices through interface mixing and rational doping, respectively. Optimization of randomness in the aperiodic superlattice to minimize its lattice thermal conductivity is also a daunting work. In this work, we define a few randomness indexes to correlate the disorder and lattice thermal conductivity, obtained from molecular dynamics simulations, of aperiodic superlattice structures, which would help screen low thermal conductivity aperiodic superlattices with no or reduced computational cost. Besides, we develop a machine learning strategy that would help us to screen low thermal conductivity aperiodic superlattice structures from numerous possibilities with a reduced computational cost.Despite the rigorous studies on superlattices in the last few decades, there are still ambiguous numerical and experimental observations regarding temperaturedependent thermal transport properties of inherently anharmonic superlattices. We bridge these opposing observations with physical understanding, which is essential to design optimized superlattices under specific working conditions. Specifically, we employ a simple yet effective two-phonon model to study the temperature-dependent changes of coherent or incoherent phonon contribution on the overall thermal transport of superlattices. Specifically, we demonstrate that the incoherent (coherent) phonon contribution in superlattices increases (decreases) with the increment of temperature, even in the classical regime. Such study on the complex temperature-dependent coherent and incoherent phonon transport in superlattices is the first study, to the best of our knowledge, and provides a possible pathway to explain the experimental results in the classical regime. We also demonstrate the feasibility of neural network based machine learning model to capture the temperature-dependent incoherent-coherent phonon transition in superlattices.