Chen, T.; Lu, W.; Li, J.; Chen, S.; Li, C.; Weng, G. J.: Tailoring tensile ductility of thin film by grain size graded substrates. International Journal of Solids and Structures 166, pp. 124 - 134 (2019)
Liu, C.; Lu, W.; Weng, G. J.; Li, J.: A cooperative nano-grain rotation and grain-boundary migration mechanism for enhanced dislocation emission and tensile ductility in nanocrystalline materials. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 756, pp. 284 - 290 (2019)
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
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…