
This repository supports the publication Vapor-solid-solid growth of single-walled carbon nanotubes. It includes the NEP$_{\text{ReC}}$ models, an ensemble of neuroevolution potentials (NEPs) developed to simulate CNT growth on rhenium catalysts, along with data and files used in the training and simulation processes. NEP$_{\text{ReC}}$ models (neps.zip): The fully trained NEP models used for production simulations. Here 1_nep.txt is used to drive the simulations and the remaining NEPs are used for error estimation (force model deviation). NEP$_{\text{ReC}}$ dataset (dataset.zip): Contains the train and test datasets for NEP$_{\text{ReC}}$ in the xyz format used by GPUMD for training. Each image is labeled with total energy in eV, force in eV/Å, and virials in eV. GPUMD files (training.zip): Input files used for training the NEP$_{\text{ReC}}$ models using GPUMD. GPUMD files (growth.zip): Input files for simulating CNT growth using GPUMD. VASP files (vasp.zip): Input files used for labeling data with VASP. CNT growth trajectory (dump_a.tar.xz): The full trajectory of the 12 μs CNT growth simulation discussed in the manuscript.
machine-learning interatomic potential, NEP, NEP_ReC, single-walled carbon nanotubes, vapor-solid-solid growth, molecular dynamics
machine-learning interatomic potential, NEP, NEP_ReC, single-walled carbon nanotubes, vapor-solid-solid growth, molecular dynamics
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