Abnormal strain-dependent thermal conductivity in biphenylene monolayer using machine learning interatomic potential

Applying tensile strain on an intrinsic lattice always results in the reduction in thermal conductivity due to the red-shift of phonon frequency and enhanced phonon anharmonicity. However, in this work, we explored an unexpected strain-enhanced thermal conductivity of a planar biphenylene network (BPN) in the frame of a Boltzmann transport equation combined with the machine learning interatomic potential. Under 5% biaxial tensile strain, the room temperature thermal conductivity of BPN reaches to about 4-5 times of that in an intrinsic sample. This phenomenon can be understood by considering a mirror symmetry induced phonon selection rule. This work highlights the significant effect of the selection rule on thermal transport and enriches the understanding of the thermal conductivity regulation in strained two-dimensional materials.

Automated summary

In this work, a surprising enhancement of thermal conductivity in a planar biphenylene network (BPN) under 5% biaxial tensile strain was explored using a Boltzmann transport equation combined with machine learning interatomic potentials. The room temperature thermal conductivity of BPN reaches about 4-5 times that of an intrinsic sample. This phenomenon can be attributed to a phonon selection rule induced by mirror symmetry. This study highlights the significant impact of the selection rule on thermal transport and enhances our understanding of thermal conductivity regulation in strained two-dimensional materials.