Kerger, P.; Rohwerder, M.; Vogel, D.: Using a Novel In-situ/Operando Chemical Cell to Investigate Surface Reactions such as the Reduction of Oxygen and Surface Oxides. AVS 63rd International Symposium & Exhibition, Nashville, TN, USA (2016)
Rohwerder, M.: Novel Approaches for Characterizing the Delamination resistance of Organic Coatings. 230th ECS Meeting-PRiME 2016, Honolulu, HI, USA (2016)
Uebel, M.; Rohwerder, M.: Conducting polymer based anticorrosion composite coatings – acceleration of the trigger signal spreading. 7th Kurt-Schwabe-Symposium 2016, Mittweida, Germany (2016)
Kerger, P.; Rohwerder, M.; Vogel, D.: Using a Novel In-situ/Operando Chemical Cell to Investigate Surface Reactions such as the Reduction of Oxygen and Surface Oxides. 11th International Symposium on Electrochemical Micro & Nanosystem Technologies (EMNT2016), Brussels, Belgium (2016)
Rohwerder, M.; Dandapani, V.: A Novel Potentiometric Approach to a Quantitative Characterization of Oxygen Reduction Kinetics at Buried Interfaces. 11th International Symposium on Electrochemical Micro & Nanosystem Technologies (EMNT2016), Brussels, Belgium (2016)
Uebel, M.; Vimalanandan, A.; Lv, L.-P.; Crespy, D.; Rohwerder, M.: Dual payload capsules for corrosion protection coatings – importance of the electronic coupling at the metal/capsules interface. 67th Annual Meeting of the International Society of Electrochemistry (ISE) 2016, The Hague, The Netherlands (2016)
Mondragon Ochoa, J. S.; Altin, A.; Rohwerder, M.; Erbe, A.: Surface Modification of Iron With Grafted Hydrophobic Acrylic Polymers and Study of Their Delamination Kinetics. Polymers and Organic Chemistry POC16, Hersonissos (Crete), Greece (2016)
Rohwerder, M.: Die Rasterkelvinsonde: neue Entwicklungen für die Charakterisierung von Korrosionsschutzbeschichtungen. 7. Korrosionsschutz-Symposium, Kloster Irsee, Germany (2016)
Rohwerder, M.: Characterization of Oxides in the Heat Affected Zone. Welding Workshop “Guidelines for use of welded stainless steel in corrosive environments” at TWI, Granta Park, Cambridge, UK (2016)
Tarzimoghadam, Z.; Rohwerder, M.; Merzlikin, S. V.; Bashir, A.; Yedra, L.; Eswara, S.; Ponge, D.; Raabe, D.: On the Role of δ phase in Hydrogen Embrittlement of Alloy 718: Multi-scale H-Mapping in a Ni–Nb Model Alloy. SINTEF and NTNU's Environmental Assisted Cracking (SNEAC) workshop, Trondheim, Norway (2016)
Wengert, A.; Swaminathan, S.; Vogel, A.; Rohwerder, M.: Internal oxidation of high strength steels during short-term annealing: Observation of unexpectedly fast progress of the internal oxidation and first tentative model. EFC Workshop High Temperature Corrosion, Frankfurt, Germany (2015)
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)
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.