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Wang, Z.; Gu, J.; An, D.; Liu, Y.; Song, M.: Characterization of the microstructure and deformation substructure evolution in a hierarchal high-entropy alloy by correlative EBSD and ECCI. Intermetallics 121, 106788 (2020)
An, X.; Wang, Z.; Ni, S.; Song, M.: The tension-compression asymmetry of martensite phase transformation in a metastable Fe40Co20Cr20Mn10Ni10 high-entropy alloy. Science China Materials 63 (9), pp. 1797 - 1807 (2020)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.