Data for: Wide-Range Predictions of Hydrogen-Dependent Vacancy Diffusion in Nickel from a near-DFT-Accurate Machine-Learning Potential
Published: 23 July 2026| Version 1 | DOI: 10.17632/r4pw8vv4z2.1
Contributors:
, Nobuyoshi Komai, , Description
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.
Files
Institutions
- The University of OsakaOsaka, Osaka
Categories
Computational Materials Science, Vacancy Defect, Hydrogen Embrittlement