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)
Rohwerder, M.: Selbstheilende Beschichtungen für den Korrosionsschutz: Ein kritischer Überblick. 28. Sitzung des AK “Korrosionsschutz durch Beschichtungen”, GfKorr, Frankfurt, Germany (2014)
Merzlikin, S. V.; Vogel, A.; Auinger, M.; Vogel, D.; Rohwerder, M.: Suppressing the selective oxidation during the recrystallization annealing of steel band for improved hot dip galvanizing: Laboratory study. ISHOC2014 - International Symposium on High-temperature Oxidation and Corrosion 2014, Hakodate, Hokkaido Japan (2014)
Vogel, D.; Borodin, S.; Merzlikin, S. V.; Keil, P.; Rohwerder, M.: Near Ambient Pressure XPS studies on the oxide formation on Fe–2Mn during thermal treatment. ISHOC2014 - International Symposium on High-temperature Oxidation and Corrosion 2014, Hakodate, Hokkaido Japan (2014)
Merzlikin, S. V.; Bashir, A.; Evers, S.; Senöz, C.; Rohwerder, M.: Using Scanning Kelvin Probe Force Microscopy and Thermal Desorption for Localized Hydrogen Detection and Quantification in Steels. 2nd International Conference on hydrogen in Steels, Gent, Belgium (2014)
Merzlikin, S. V.; Bashir, A.; Rohwerder, M.: Hydrogen embrittlement and traps structure of advanced high strength sheet steel for automotive applications. ICH2P-2014, International Conference on Hydrogen Production, Fukuoka, Japan. (2014)
Rohwerder, M.: Scanning Kelvin Probe Force Microscopy as Tool for the Investigation of Localized Corrosion. 2014 ECS and SMEQ Joint Internat. Meeting, Cancun, Mexico (2014)
Rohwerder, M.: Self-Healing Coatings for Corrosion Protection: A Critical Overview and Latest Results. Gordon Reserach Conference on Aqueous Corrosion , New London, AR, USA (2014)
Rohwerder, M.: Korrosionsschutz mit leitfähigen Polymeren: Entwicklung selbstheilender Beschichtungen. Materials Valley Workshop, Hanau, Germany (2014)
Rohwerder, M.: Zinc alloy coatings for corrosion protection: From the basics to new challenges. MSE Colloquium, The Ohio State University, Columbus, Columbus, OH, USA (2014)
Rohwerder, M.: A new technique for high-sensitive and spatially resolved detection of hydrogen and its application in corrosion science steel. Hydrogen Embrittlement Workshop, Düsseldorf, Germany (2014)
Rohwerder, M.; Borodin, S.; Vogel, A.; Vogel, D.: Investigation of the Fundamental Processes in the Internal Oxidation of Binary and Ternary Iron Based Alloys at Elevated Temperatures. 2014 ECS and SMEQ Joint Internat. Meeting, Cancun, Mexico (2014)
Altin, A.; Erbe, A.; Ritter, H.; Rohwerder, M.: Controlled release of inhibitors from composite organic coatings: A “green” way of corrosion protection. EUROCORR 2013, Estoril, Portugal (2013)
Vimalanandan, A.; Lv, L. P.; Zhao, Y.; Landfester, K.; Crespy, D.; Rohwerder, M.: Active corrosion protection coatings based on potential triggered release systems. EUROCORR 2013, the European Corrosion Congress, “For a blue sky”, Estoril, Portugal (2013)
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…