Jovičević-Klug, P.; Jovičević-Klug, M.; Tegg, L.; Hester, J. R.; Čapek, J.; Polatidis, E.; Cairney, J. M.; McCord, J.; Rohwerder, M.: Operando cryogenic processing effects on residual stress and magnetism of martensitic stainless steel for energy sector. Materials today advances 31, 100895 (2026)
Vijayshankar, D.; Rohwerder, M.: Revealing the Role of Oxygen Reduction Rate and Degradation Time on the Buried Metal/Coating Interphase Delamination Behavior. Corrosion 81 (12), pp. 1145 - 1150 (2025)
Wang, L.; Sam, H. C.; Ao, M.; Rohwerder, M.; Dong, C.: The effect of austenite phase transformation on hydrogen distribution and embrittlement mechanisms of heterogeneous martensite stainless steel manufactured by laser powder bed fusion. Corrosion Science 256, 113195 (2025)
Jovičević-Klug, M.; Brondin, C. A.; Caretta, A.; Bonnekoh, C.; Gossing, F.; Vogel, A.; Rieth, M.; McCord, J.; Rohwerder, M.; Jovičević-Klug, P.: Suppression of Cr nanoclusters and enrichments in Fe–Cr based alloys with cryogenic processing for future energy sector. Journal of Materials Research and Technology 36, pp. 9262 - 9273 (2025)
Khayatan, N.; Prabhakar, J. M.; Jalilian, E.; Madelat, N.; Terryn, H.; Rohwerder, M.: On the rate determining step of cathodic delamination of delamination-resistant organic coatings. Corrosion Science 239, 112396 (2024)
Azzam, W.; Subaihi, A.; Rohwerder, M.; Bashir, A.; Terfort, A.; Zharnikov, M.: Odd-even effects in aryl-substituted alkanethiolate SAMs: nonsymmetrical attachment of aryl unit and its impact on the SAM structure. Physical Chemistry Chemical Physics 26 (9), pp. 7563 - 7572 (2024)
Ravikumar, A.; Höche, D.; Feiler, C.; Lekka, M.; Salicio-Paz, A.; Rohwerder, M.; Prabhakar, J. M.; Zheludkevich, M.: Exploring the Effect of Microstructure and Surface Recombination on Hydrogen Effusion in Zn–Ni-Coated Martensitic Steels by Advanced Computational Modeling. Steel Research International 95 (2), 2300353 (2024)
Venkatachalam, D.; Govindaraj, Y.; Prabhakar, J. M.; Ganapathi, A.; Sakairi, M.; Rohwerder, M.; Neelakantan, L.: Smart release of turmeric as a potential corrosion inhibitor from a pH-responsive polymer encapsulated highly ordered mesoporous silica containers. Surfaces and Interfaces 45, 103883 (2024)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
A novel design with independent tip and sample heating is developed to characterize materials at high temperatures. This design is realized by modifying a displacement controlled room temperature micro straining rig with addition of two miniature hot stages.
Many important phenomena occurring in polycrystalline materials under large plastic strain, like microstructure, deformation localization and in-grain texture evolution can be predicted by high-resolution modeling of crystals. Unfortunately, the simulation mesh gets distorted during the deformation because of the heterogeneity of the plastic…
In this project we developed a phase-field model capable of describing multi-component and multi-sublattice ordered phases, by directly incorporating the compound energy CALPHAD formalism based on chemical potentials. We investigated the complex compositional pathway for the formation of the η-phase in Al-Zn-Mg-Cu alloys during commercial…
The project HyWay aims to promote the design of advanced materials that maintain outstanding mechanical properties while mitigating the impact of hydrogen by developing flexible, efficient tools for multiscale material modelling and characterization. These efficient material assessment suites integrate data-driven approaches, advanced…
The Atom Probe Tomography group in the Microstructure Physics and Alloy Design department is developing integrated protocols for ultra-high vacuum cryogenic specimen transfer between platforms without exposure to atmospheric contamination.
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.