Brink, T.; Pemma, S.; Choi, Y.; Dehm, G.: From the microscopic to the macroscopic grain boundary: The role of line defects. 2026 MRS Spring Meeting & Exhibit, Honolulu, HI, USA (2026)
Brink, T.; Kim, D.; Dehm, G.: Atomistic computer simulations of the influence of grain boundary phases on segregation. DPG-Frühjahrstagung 2025, Regensburg, Germany (2025)
Lee, J. S.; Riedel, J. L.; Kauffmann, A.; Guth, S.; Heilmaier, M.; Kanjilal, A.; Dehm, G.; Best, J. P.; Stein, F.: The Effect of Vacancy Concentration on the Micromechanical Behaviour of B2 FeAl Diffusion Couples Covering a Wide Range of Compositions. Intermetallics 2025, Bad Staffelstein, Germany (2025)
Dehm, G.: Effects of Grain Boundary Structure and Chemistry on Plasticity in Metals. Nanomechanical Testing in Materials Research and Development IX, Messina (Sicily), Italy (2024)
Dehm, G.: Towards Understanding Dislocation Strengthening Mechanisms of Cr(Mn)FeCoNi high entropy alloys by advanced (S)TEM. ELMINA 2024, Belgrade, Serbia (2024)
Zhou, X.; Hickel, T.; Gault, B.; Ophus, C.; Liebscher, C.; Dehm, G.; Raabe, D.: Exploring the Relationship Between Grain Boundary Structure and Chemical Composition at the Atomic Level. International Conference on Intergranular and Interphase Boundaries in Materials (IIB 2024), Beijing, China (2024)
Dehm, G.: Atomic resolved imaging of grain boundary phase transitions in pure and alloyed metallic thin films. 17th International Conference on Intergranular and Interphase Boundaries in Materials (IIB 2024), Beijing, China (2024)
Lee, J. S.; Dehm, G.; Best, J. P.; Stein, F.: A Micromechanical Study on the Correlation of Composition and Properties of B2 FeAl across the Interface of an Fe–Al Diffusion Couple. ECR Day, Ruhr Universität Bochum, Bochum, Germany (2024)
Dehm, G.; Devulapalli, V.; Schulz, F.; Soares Barreto, E.; Ellendt, N.; Jägle, E. A.: Strengthening of CoCrFe(Mn)Ni high entropy alloys by dislocation pinning: From Lattice friction & SRO to particle strengthening. Possibilities and Limitations of Quantitative Materials Modeling and Characterization 2024, Bernkastel-kues, Germany (2024)
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
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.
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…