Ou, P.; Li, Z.: Ordering of primary carbonitrides in an austenitic steel revealed by transmission electron microscopy and atom probe tomography. Materials 11 (11), 2321 (2018)
Li, Z.; Raabe, D.: Influence of compositional inhomogeneity on mechanical behavior of an interstitial dual-phase high-entropy alloy. Materials Chemistry and Physics 210, pp. 29 - 36 (2018)
Luo, H.; Li, Z.; Mingers, A. M.; Raabe, D.: Corrosion behavior of an equiatomic CoCrFeMnNi high-entropy alloy compared with 304 stainless steel in sulfuric acid solution. Corrosion Science 134, pp. 131 - 139 (2018)
Wang, M.; Li, Z.; Raabe, D.: In-situ SEM observation of phase transformation and twinning mechanisms in an interstitial high-entropy alloy. Acta Materialia 147, pp. 236 - 246 (2018)
Luo, H.; Li, Z.; Chen, Y.-H.; Ponge, D.; Rohwerder, M.; Raabe, D.: Hydrogen effects on microstructural evolution and passive film characteristics of a duplex stainless steel. Electrochemistry Communucations 79, pp. 28 - 32 (2017)
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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
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.