Dresden 2026 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 19: Poster Session
MM 19.25: Poster
Tuesday, March 10, 2026, 18:00–20:00, P5
Correlating Atomic Structure and Grain Boundary Energy in the 5D Space of Degrees of Freedom — •Mahkam Madadi1, Timo Schmalofski1, Martin Kroll2, Holger Dette3, and Rebecca Janisch1 — 1ICAMS, Ruhr-University Bochum, Germany — 2Chair of Stochastics and Machine Learning, University of Bayreuth, Germany — 3Chair of Stochastics, Ruhr-University Bochum, Germany
In materials design, it is essential to develop grain boundary (GB) models that effectively relate the atomic structure to the macroscopic properties of grain boundaries. Grain boundaries are categorized by five degrees of freedom (DOF), which include grain boundary plane orientation, rotation axis, and the misorientation angles between adjacent grains. On the other hand, the energy associated with these boundaries is governed by microscopic state, including atomic positions and structural units . This study seeks to investigate the correlation between the microscopic structure and the energy of grain boundaries. Previous studies often explored only a subset of these DOFs, typically by varying one or two while keeping the others fixed. In contrast, this study employs molecular statics in combination with a statistical approach [1] to assess the complete 5D space of DOFs and to model and analyze the microstructure-energy relationship.
[1] T. Schmalofski, M. Kroll, H. Dette, and R. Janisch. Towards active learning: A stopping criterion for the sequential sampling of grain boundary degrees of freedom. Materialia, 31:101865, 2023. https://doi.org/10.1016/j.mtla.2023.101865
Keywords: Grain Boundary; Grain Boundary Energy; Macroscopic Degrees of Freedom; Molecular Statics; Machine Learning