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

Dataset for the paper:Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs. https://doi.org/10.48550/arXiv.2410.13535 fig1-polycrystalline_graphene_models.zip: Data of polycrystalline graphene models in extended XYZ format, used in the paper. These models are sourced from [1]. fig2-speed_4090_4070.zip:Input and output data for testing the speed performance of the NVIDIA 4090 and 4070 graphics cards. fig3-grain_boundary_energy .zip: Data for the bicrystal graphene models used in the paper, sourced from [2]. fig4_and_fig5-thermal_conductivity_of_polycrystalline_graphene.zip: Input and output data for the thermal conductivity calculations of pristine graphene and polycrystalline graphene with different grain sizes,computed using the HNEMD method. fig6-strain_effect.zip: Input and output data for the effects of applied strain on pristine graphene and polycrystalline graphene, both with and without stress. This repository provides essential data for the simulations and analyses discussed in the article. If you have any questions or need further information, please refer to the respective references or contact the authors. [1] Z. Fan, P. Hirvonen, L. F. C. Pereira, M. M. Ervasti, K. R. Elder, D. Donadio, A. Harju, and T. Ala-Nissila, Nano Letters 17, 5919 (2017). https://pubs.acs.org/doi/10.1021/acs.nanolett.7b01742 [2]K. Azizi, P. Hirvonen, Z. Fan, A. Harju, K. R. Elder, T. Ala-Nissila, and S. M. V. Allaei, Carbon 125, 384 (2017) https://www.sciencedirect.com/science/article/abs/pii/S0008622317309351?via%3Dihub