Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs

First-principles molecular dynamics simulations of heat transport in systems with large-scale structural features are challenging due to their high computational cost. Here, using polycrystalline graphene as a case study, we demonstrate the feasibility of simulating heat transport with near first-principles accuracy in systems containing over 1.4 million atoms, achievable even with consumer desktop GPUs. This is enabled by the highly efficient neuroevolution potential (NEP) approach, as implemented in the open-source GPUMD package. Leveraging the NEP model's accuracy and efficiency, we quantify the reduction in thermal conductivity of polycrystalline graphene due to grain boundaries with varying grain sizes, resolving contributions from in-plane and out-of-plane (flexural) phonon modes. Additionally, we find that grain boundaries can lead to finite thermal conductivity even under significant tensile strain, in contrast to the divergent behavior observed in pristine graphene under similar conditions, indicating that grain boundaries may play a crucial role in thermal transport in low-dimensional momentum-conserving systems. These findings could offer insights for interpreting experimental observations, given the widespread presence of both large-scale grain boundaries and external strains in real materials. The demonstrated ability to simulate millions of atoms with near-first-principles accuracy on consumer desktop GPUs using the NEP approach will help make large-scale high-fidelity atomistic simulations more accessible to the broader research community.