Machine-learning discovery of extreme coherent thermal transport governed by motif-level order in Si-Ge superlattices

Controlling phonon-mediated heat transport in multilayer nanostructures is central to thermal management and energy-conversion technologies. Yet identifying architectures exhibiting extreme lattice thermal conductivity ( κ l ) remains challenging due to the combinatorial complexity of interface arrangements and the high computational cost of transport simulations. In superlattices, thermal transport arises from competing wave effects: coherent phonon propagation enhances transport, whereas interface scattering and localization suppress it. We study Si-Ge superlattices using an iterative machine-learning-guided non-equilibrium Green’s function (NEGF) approach, where a convolutional neural network trained on NEGF data efficiently explores an experimentally constrained design space to identify both low- and high- κ l limits. Dense interface clustering induces cumulative elastic scattering and reduces κ l by up to 35%, while sparse, locally ordered motifs preserve extended phonon modes and enhance κ l by up to 33% beyond periodic designs. These results are obtained at 200 K in a regime where phonon transport is predominantly coherent and elastic; thus, the identified structural trends are representative of the coherent transport regime. Our work reveals that local motif-level order, rather than global periodicity, governs extreme thermal transport in multilayer systems.