Li, Y. S.; Niu, Y.; Spiegel, M.: High temperature interaction of Al/Si-modified Fe–Cr alloys with KCl. Corrosion Science 49 (4), pp. 1799 - 1815 (2007)
Li, Y. S.; Spiegel, M.; Shimada, S.: Corrosion behaviour of model alloys with NaCl–KCl coating. Materials Chemistry and Physics 93 (1), p. 217 - 217 (2005)
Li, Y. S.; Spiegel, M.: Models describing the degradation of FeAl and NiAl alloys induced by ZnCl2/KCl melt at 400-450 °C. Corrosion Science 46, 8 (2004)
Li, Y. S.; Spiegel, M.: Degradation performance of Al-containing alloys and intermetallics by molten ZnCl2/KCl. In: Corrosion Science in the 21th Century, 1. UMIST, Manchester, UK (2003)
Li, Y. S.; Spiegel, M.: Degradation performance of Al-containing alloys and intermetallics by molten ZnCl2/KCl. Corrosion Science in the 21th Century, UMIST Manchester, UK (2003)
Li, Y. S.; Spiegel, M.: High temperature interactions of pure Cr with KCl. 6th Int. Symposium on High temperature Corrosion and Protection of Materials, Lez Embiez, France (2004)
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
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…
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…