Li, Y.; Fang, X.; Zhang, S.; Feng, X.: Microstructure evolution of FeNiCr alloy induced by stress-oxidation coupling using high temperature nanoindentation. Corrosion Science 135, pp. 192 - 196 (2018)
Yue, M.; Dong, X.; Fang, X.; Feng, X.: Effect of interface reaction and diffusion on stress-oxidation coupling at high temperature. Journal of Applied Physics 123 (15), 155301 (2018)
Fang, X.; Dong, X.; Jiang, D.; Feng, X.: Modification of the mechanism for stress-aided grain boundary oxidation ahead of cracks. Oxidation of Metals 89 (3-4), pp. 331 - 338 (2018)
Lu, S.-Y.; Chen, Y.; Fang, X.; Feng, X.: Hydrogen peroxide sensor based on electrodeposited Prussian blue film. Journal of Applied Electrochemistry 47 (11), pp. 1261 - 1271 (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.