Shah, V.; Sedighiani, K.; Van Dokkum, J. S.; Bos, C.; Roters, F.; Diehl, M.: Coupling crystal plasticity and cellular automaton models to study meta- dynamic recrystallization during hot rolling at high strain rates. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 849, 143471 (2022)
Shah, V.; Krugla, M.; Offerman, S. E.; Sietsma, J.; Hanlon, D. N.: Effect of silicon, manganese and heating rate on the ferrite recrystallization kinetics. ISIJ International 60 (6), pp. 1312 - 1323 (2020)
Shah, V.; Diehl, M.; Roters, F.: Prediction of Nucleation Sites for Recrystallization using Crystal Plasticity Simulations. 7th International Conference on Recrystallization and Grain Growth, Ghent, Belgium (2019)
Shah, V.; Diehl, M.; Roters, F.: Prediction of Nucleation Sites During Recrystallization. M2i conference “Meeting Materials”, Noordwijkerhout, The Netherlands (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
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.
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…