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MatterialLetter_"Self-diffusion in Symmetrical Tilt <100> Grain Boundaries in Tungsten: Molecular dynamics simulation"

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NIAID Data Ecosystem2026-05-02 收录
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https://data.mendeley.com/datasets/8kj8rh6c98
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The repository presents data used and obtained as a result of the work on modeling grain boundary self-diffusion in tungsten. The data is presented as follows: The 'Jup_*.ipynb' file format contains code written in the JupyterNotebook development environment for processing data obtained as a result of modeling Description of the files: 'Jup_PointDefect.ipynb' - Processing of the results of calculations of point defects; 'Jup_General.ipynb' - Processing of the results of the diffusion calculation; 'Jup_Int.ipynb' - Processing of the results of the diffusion calculation by the interstitial mechanism; 'Jup_Vac.ipynb' - Processing of the results of the diffusion calculation by the vacancy mechanism; To reproduce the simulation, you need to install the LAMMPS program and run it according to the startup rule; Folders: '1_Diffuse' - contains data on the calculation of diffusion: * Folder 'Input_DATA' - contains the input diffusion calculation file for each mechanism; * Folder 'Output_DATA' - contains the text output of this simulation; '1_PD' - Contains data for point defect calculations (VFE- vacancies, IFE - interstitil (SIA)): * 'IFE_Run_001.ipynb' and 'VFE_Run_001.ipynb' - contains the code that implements the automated calculation of point defects. * Folder 'VacForEn_data' and 'IntForEn_data' - contain the output files of point defect calculations; '1_Structure' - Contains data for calculating the structure of the GB: * Folder 'Structure' - Contains the structure of the GB corresponding to the minimum formation energy; * Folder 'y-Surf_DATA' - Contains input files for calculating and obtaining the minimum energy structure using the y-surface method; 'CSV_Data' - contains output data processed in Python in .csv format; 'Picture' - содержит картинки графиков полученные обработкой в JupyterNotebook; P.S. The calculations were performed using an AMD Ryzen9 5950x processor and an NVIDIA RTX 3060 graphics accelerator.
创建时间:
2024-07-31
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