Sridar, S.; Wang, Z.; Xiong, W.: Laser powder bed fusion of high-strength low-alloy steels with post-heat treatment design for enhanced mechanical performance. Materials Science and Engineering: A 895, 146210 (2024)
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)
Wang, Z.; Lu, W.; Raabe, D.; Li, Z.: On the mechanism of extraordinary strain hardening in an interstitial high-entropy alloy under cryogenic conditions. Journal of Alloys and Compounds 781, pp. 734 - 743 (2019)
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
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.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.