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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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…