Pan, Y.; Dong, A.; Zhou, Y.; Antonov, S.; Chen, Z.; Du, D.; Sun, B.: Synergistic enhancement of high temperature strength and ductility with a novel g/e dual-phase hetero-nanostructure in NiCoCr-based alloys. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 877, 145142 (2023)
Zhu, Y.; Heo, T. W.; Rodriguez, J. N.; Weber, P. K.; Shi, R.; Baer, B. J.; Morgado, F. F.; Antonov, S.; Kweon, K. E.; Watkins, E. B.et al.; Savage, D. J.; Chapman, J. E.; Keilbart, N. D.; Song, Y.; Zhen, Q.; Gault, B.; Vogel, S. C.; Sen-Britain, S. T.; Shalloo, M. G.; Orme, C.; Bagge-Hansen, M.; Hahn, C.; Pham, T. A.; Macdonald, D. D.; Qiu, R. S.; Wood, B. C.: Hydriding of titanium: Recent trends and perspectives in advanced characterization and multiscale modeling. Current Opinion in Solid State and Materials Science 26, 101020 (2022)
Zhang, C.; Yu, H.; Antonov, S.; Li, W.; He, J.; Zhi, H.; Su, Y.: Alleviating the strength-ductility trade-off dilemma in high manganese steels after hydrogen charging by adjusting the gradient distribution of twins. Corrosion Science 207, 110579 (2022)
Tan, Q.; Yan, Z.; Li, R.; Ren, Y.; Wang, Y.; Gault, B.; Antonov, S.: In-situ synchrotron-based high energy X-ray diffraction study of the deformation mechanism of δ-hydrides in a commercially pure titanium. Scripta Materialia 213, 114608 (2022)
Tan, Q.; Yan, Z.; Wang, H.; Dye, D.; Antonov, S.; Gault, B.: The role of β pockets resulting from Fe impurities in hydride formation in titanium. Scripta Materialia 213, 114640 (2022)
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…
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.