Qin, Y.; Li, J.; Herbig, M.: Microstructural origin of the outstanding durability of the high nitrogen bearing steel X30CrMoN15-1. Materials Characterization 159, 110049 (2020)
Srikakulapu, K.; Qin, Y.; Sreekala, L.; Morsdorf, L.; Herbig, M.: On the decomposition resistance of carbonitride precipitates during high-pressure torsion in X30CrMoN15-1 bearing steel. High Nitrogen Steel conference, HNS 2021, online, Shanghai, China (2021)
Qin, Y.; Mayweg, D.; Tung, P.-Y.; Pippan, R.; Herbig, M.: Mechanism of cementite decomposition in 100Cr6 bearing steels during high pressure torsion. MSE Congress 2020, virtual, Sankt Augustin, Germany (2020)
Qin, Y.: Effect of post-heat treatment on the microstructure and mechanical properties of SLM-produced IN738LC. Master, RWTH Aachen, Aachen, Germany (2017)
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
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…
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.
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…
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.