Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach
收藏NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://zenodo.org/record/14605663
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资源简介:
Readme
Structure file: relax_12g_mg_x150y150z150.data
ESD features files for different elements: info_*.txt
with column names header: 'id', 'x', 'y', 'z', 'Potential Energy', '$σ_{xx}$', '$σ_{yy}$', '$σ_{zz}$', '$σ_{xy}$', '$σ_{xz}$', '$σ_{yz}$', '$σ_{h}$', 'Coordination', 'CSP', 'Atomic Volume', 'Cavity Radius', 'Surface Area', '$F_{vib}^{600K}$', '$MSD_{vib}$', '$V_{flex}$', '$ΔF_{seg}^{vib,600K}$', '$ΔE_{seg}$', '$ΔF_{seg}^{600K}$'
SOAP features file: GB_SOAP_Mg.npy
创建时间:
2025-01-06



