Designing Nanostructures for Phonon Transport via Bayesian Optimization

We demonstrate optimization of thermal conductance across nanostructures by\ndeveloping a method combining atomistic Green's function and Bayesian\noptimization. With an aim to minimize and maximize the interfacial thermal\nconductance (ITC) across Si-Si and Si-Ge interfaces by means of Si/Ge composite\ninterfacial structure, the method identifies the optimal structures from\ncalculations of only a few percent of the entire candidates (over 60,000\nstructures). The obtained optimal interfacial structures are non-intuitive and\nimpacting: the minimum-ITC structure is an aperiodic superlattice that realizes\n50% reduction from the best periodic superlattice. The physical mechanism of\nthe minimum ITC can be understood in terms of crossover of the two effects on\nphonon transport: as the layer thickness in superlattice increases, the impact\nof Fabry-P\\'erot interference increases, and the rate of reflection at the\nlayer-interfaces decreases. Aperiodic superlattice with spatial variation in\nthe layer thickness has a degree of freedom to realize optimal balance between\nthe above two competing mechanism. Furthermore, aperiodicity breaks the\nconstructive phonon interference between the interfaces inhibiting the coherent\nphonon transport. The present work shows the effectiveness and advantage of\nmaterial informatics in designing nanostructures to control heat conduction,\nwhich can be extended to other interfacial structures.\n