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)
Li, Z.; Sun, Y.; Lavernia, E. J.; Shan, A.: Mechanical Behavior of Ultrafine-Grained Ti–6Al–4V Alloy Produced by Severe Warm Rolling: The Influence of Starting Microstructure and Reduction Ratio. Metallurgical and Materials Transactions a-Physical Metallurgy and Materials Science 46 (11), pp. 5047 - 5057 (2015)
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
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.