A dataset combining microcompression and nanoindentation data from finite element simulations of nanoporous metals

Nanoporous metals with their complex microstructure represent an ideal candidate for method developments that combine physics, data and machine learning. They allow to tune the solid fraction, ligament size and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical properties. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure-property relationships. Efficient finite element beam modeling techniques are used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A data base is provided that uses consistent settings of structural and mechanical properties on the microscale for which the elastic-plastic macroscopic compression behavior and the hardness is predicted. Ligament geometries of two different initial solid fractions are chosen, for which the structural randomization, the connectivity density, the yield stress and the work hardening rate are randomly varied in large ranges. This data base allows deriving the microstructure-properties relationships of nanoporous metals by means of dimensionality reduction, data mining and machine learning.