Physics mechanisms underlying the optimization of coherent heat transfer across width-modulated nanowaveguides with calculations and machine learning.

Optimization of heat transfer at the nanoscale is necessary for efficient modern technology applications in nanoelectronics, energy conversion, and quantum technologies. In such applications, phonons dominate thermal transport and optimal performance requires minimum phonon conduction. Coherent phonon conduction is minimized by maximum disorder in the aperiodic modulation profile of width-modulated nanowaveguides, according to a physics rule. It is minimized for moderate disorder against physics intuition in composite nanostructures. Such counter behaviors call for a better understanding of the optimization of phonon transport in non-uniform nanostructures. We have explored mechanisms underlying the optimization of width-modulated nanowaveguides with calculations and machine learning, and we report on generic behavior. We show that the distribution of the thermal conductance among the aperiodic width-modulation configurations is controlled by the modulation degree irrespective of choices of constituent material, width-modulation-geometry, and composition constraints. The efficiency of Bayesian optimization is evaluated against increasing temperature and sample size. It is found that it decreases with increasing temperature due to thermal broadening of the thermal conductance distribution. It shows weak dependence on temperature in samples with high discreteness in the distribution spectrum. Our work provides new physics insight and indicates research pathways to optimize heat transfer in non-uniform nanostructures.