Lee, J. S.; Riedel, J. L.; Schweizer, P.; Kauffmann, A.; Heilmaier, M.; Dehm, G.; Best, J. P.; Kanjilal, A.; Stein, F.: Influence of point defects on the hardness and reduced modulus of B2-ordered FeAl. Journal of Alloys and Compounds 1065, 188036 (2026)
Kanjilal, A.; Aliramaji, S.; Neuß, D.; Hans, M.; Schneider, J. M.; Best, J. P.; Dehm, G.: Microscale deformation of an intermetallic-metal interface in bi-layered film under shear loading. Scripta Materialia 263, 116665 (2025)
Best, J. P.; Gibson, J. S. K. L.; Lawrence, S.; Lee, S.; Lee, S.-W.: Nanoindentation's imprint on an advanced society: Toward application conditions at the extremes. MRS Bulletin 50 (6), pp. 695 - 704 (2025)
Dubosq, R.; Woods, E.; Gault, B.; Best, J. P.: Correction: Electron microscope loading and in situ nanoindentation of water ice at cryogenic temperatures. PLOS ONE 19 (6), e0306374 (2024)
Jentner, R.; Scholl, S.; Srivastava, K.; Best, J. P.; Kirchlechner, C.; Dehm, G.: Local strength of bainitic and ferritic HSLA steel constituents understood using correlative electron microscopy and microcompression testing. Materials and Design 236, 112507 (2023)
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
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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…