Uebel, M.; Exbrayat, L.; Rabe, M.; Tran, T. H.; Crespy, D.; Rohwerder, M.: On the Role of Trigger Signal Spreading Velocity for Efficient Self-Healing Coatings for Corrosion Protection. Journal of the Electrochemical Society 165 (16), pp. C1017 - C1027 (2018)
Dandapani, V.; Tran, T. H.; Bashir, A.; Evers, S.; Rohwerder, M.: Hydrogen Permeation as a Tool for Quantitative Characterization of Oxygen Reduction Kinetics at Buried Metal-Coating Interfaces. Electrochimica Acta 189, pp. 111 - 117 (2016)
Tran, T. H.; Gerlitzky, C.; Rohwerder, M.; Groche, P.: Which properties must a surface have to be suitable for cold pressure welding? 22nd International Conference on Material Forming (ESAFORM 2019), Mondragon Unibrtsitatae, Spain, May 08, 2019 - May 10, 2019. AIP Conference Proceedings 2113, 050019, (2019)
Uebel, M.; Tran, T. H.; Altin, A.; Gerlitzky, C.; Erbe, A.; Groche, P.: Which Properties Must a Surface have to be Suitable for Cold Pressure Welding? 22nd International Conference on Material Forming (ESAFORM 2019), Mondragon Unibrtsitatae, Spain (2019)
Rohwerder, M.; Tran, T. H.: Novel zinc-nanocontainer composite coatings for intelligent corrosion protection. 11th Intrenational Conference on Zinc And Zinc Alloy Coated Steel Sheet- GALVATECH 2017, The University of Tokyo, Tokyo, Japan (2017)
Uebel, M.; Vimalanandan, A.; Tran, T. H.; Rohwerder, M.: Coatings for intelligent self-healing of macroscopic defects: first results and the major challenges. eMRS, Symposium „Self-Healing Materials", Warsaw, Poland (2015)
Uebel, M.; Exbrayat, L.; Rabe, M.; Tran, T. H.; Crespy, D.; Rohwerder, M.: Role of Trigger Signal Spreading Velocity on Self-healing Capability of Intelligent Coatings for Corrosion Protection. Scientific Advisory Board Meeting 2019, 6-years Evaluation of the Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany (2019)
Vimalanandan, A.; Altin, A.; Tran, T. H.; Rohwerder, M.: Conducting Polymers for Corrosion Protection - Raspberry like shaped ICP “pigments”. Gordon Research Conference Corrosion-Aqueous, New London, NH, USA (2012)
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.