Data for: Wide-Range Predictions of Hydrogen-Dependent Vacancy Diffusion in Nickel from a near-DFT-Accurate Machine-Learning Potential

This archive provides the reproducibility materials associated with the manuscript “Wide-Range Predictions of Hydrogen-Dependent Vacancy Diffusion in Nickel from a near-DFT-Accurate Machine-Learning Potential.” It contains the numerical data underlying the manuscript figures and diffusion maps, representative EHTI MD/GCMC input files, the trained MTP potential for the Ni–H system, and the corresponding training and validation datasets.本数据归档包提供了与题为《基于近密度泛函（DFT, Density Functional Theory）精度机器学习势的镍中氢依赖性空位扩散的宽范围预测》的学术论文相关的可复现研究材料。该归档包含支撑该论文所有配图与扩散映射图的数值数据、代表性的EHTI分子动力学（MD, Molecular Dynamics）/巨正则蒙特卡洛（GCMC, Grand Canonical Monte Carlo）输入文件、镍-氢（Ni-H）体系的训练完成的MTP势，以及对应的训练集与验证数据集。