Machine learning revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene - Jingwen Wang; Zheng Zhu; Tianran Jiang; Ke Chen (npj Computational Materials, 2025)
Abstract In two-dimensional (2D) layer-stacked materials, the twist angle between layers provides extensive freedom to explore novel physics and engineer remarkable thermal transport properties. We discovered that the cross-plane thermal conductivity of multilayer graphene can be effectively controlled by arranging the layers with two specific twist angles in a defined sequence. Disorderly aperiodic twisted graphene layers lead to the localization of phonons, substantially reducing the cross-plane thermal transport via the interference of coherent phonons. We employed non-equilibrium molecular dynamics simulations combined with machine learning approach, to study heat transport in the two-angle disordered multilayer stacks, and identified within the constrained structural space the optimal stacking sequence that can minimize the cross-plane thermal conductivity. Compared to pristine graphite, the optimized structure can reduce thermal conductivity by up to 80%. Through analysis of phonon transport properties across different structures, we revealed the underlying physical mechanism of phonon localization.