Lill, K. A.; Fushimi, K.; Hassel, A. W.; Seo, M.: Investigations on the kinetics of single grains and grain boundaries by use of Scan-ning Electrochemical Microscopy (SECM). 6th International Symposium on Electrochemical Micro & Nanosystem Technologies, Bonn, Germany (2006)
Mardare, A. I.; Lill, K. A.; Wieck, A.; Hassel, A. W.: 3D Scanning Setup for High Throughput Measurements. 6th International Symposium on Electrochemical Micro & Nanosystem Technologies, Bonn, Germany (2006)
Lill, K. A.; Stratmann, M.; Frommeyer, G.; Hassel, A. W.: Investigations on anisotropy of nickelfree alloys with combined local and trace analysis. GDCh Jahrestagung 2005, Fachgruppe Angewandte Elektrochemie, Düsseldorf, Germany (2005)
Lill, K. A.; Hassel, A. W.; Stratmann, M.: Korrosionsuntersuchungen auf einzelnen Körnern einer neuen Klasse ferritischer FeAlCr Leichtbaustähle. 79. AGEF Seminar - 25 Jahre Elektrochemie in Düsseldorf, Düsseldorf, Germany (2004)
Lill, K.; Hassel, A. W.: On the corrosion resistance of single grains of a new class of FeCrAl light weight ferritic steels. 5th International Symposium on Electrochemical Micro & Nanosystem Technologies, Tokyo, Japan (2004)
Lill, K.; Hassel, A. W.; Stratmann, M.: Electrochemical and corrosion investigations on LIP-steel and austenitic model steels of similar composition. GDCH Jahrestagung 2003, Fachgruppe Angewandte Elektrochemie mit 8. Grundlagensymposium der GDCh, DECHEMA, DBG, München, Germany (2003)
Lill, K. A.: Electrochemical Investigations on the Corrosion Properties of New Classes of Light Weight Steels. Dissertation, Ruhr-Universität-Bochum, Bochum, Germany (2008)
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…
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.