Choi, W. S.; De Cooman, B. C.: Effect of Carbon on the Damping Capacity and Mechanical Properties of Thermally Trained Fe–Mn Based High Damping Alloys. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 700, pp. 641 - 648 (2017)
Lee, C. W.; Choi, W. S.; Cho, Y. R.; De Cooman, B. C.: Direct Resistance Joule Heating of Al-10 pct Si-Coated Press Hardening Steel. Metallurgical and Materials Transactions A 47 (6), pp. 2875 - 2884 (2016)
Lee, C. W.; Choi, W. S.; Cho, Y. R.; De Cooman, B. C.: Microstructure evolution of a 55 wt.% Al–Zn coating on press hardening steel during rapid heating. Surface and Coatings Technology 281, pp. 35 - 43 (2015)
Choi, W. S.; De Cooman, B. C.; Sandlöbes, S.; Raabe, D.: Size and orientation effects in partial dislocation-mediated deformation of twinning-induced plasticity steel micro-pillars. Acta Materialia 98, 12304, pp. 391 - 404 (2015)
Choi, W. S.: Deformation mechanisms and the role of interfaces in face-centered cubic Fe-Mn-C micro-pillars. Dissertation, RWTH Aachen, Aachen, Germany (2018)
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
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.
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…