Diehl, M.: Crystal Plasticity Simulations on Real Data: Towards Highly Resolved 3D Microstructures. Seminar des Instituts für Mechanik, KIT, Karlsruhe, Germany (2016)
Roters, F.; Diehl, M.; Shanthraj, P.: Crystal Plasticity Simulations - Fundamentals, Implementation, Application. Micromechanics of Materials, Zernike Institute for Advanced Materials, University of Groningen
, Groningen, The Netherlands (2016)
Roters, F.; Diehl, M.; Shanthraj, P.: DAMASK Evolving From a Crystal Plasticity Subroutine Towards a Multi-Physics Simulation Tool. Focus Group Meeting “Metals”, SPP 1713, Bad Herrenalb, Germany (2016)
Roters, F.; Zhang, C.; Eisenlohr, P.; Shanthraj, P.; Diehl, M.: On the usage of HDF5 in the DAMASK crystal plasticity toolkit. 2nd International Workshop on Software Solutions for Integrated Computational Materials Engineering - ICME 2016, Barcelona, Spain (2016)
Cereceda, D.; Diehl, M.; Roters, F.; Raabe, D.; Perlado, J. M.; Marian, J.: An atomistically-informed crystal plasticity model to predict the temperature dependence of the yield strength of single-crystal tungsten. XXV International Workshop on Computational Micromechanics of Materials, Bochum, Germany (2015)
Diehl, M.; Eisenlohr, P.; Roters, F.; Shanthraj, P.; Reuber, J. C.; Raabe, D.: DAMASK: The Düsseldorf Advanced Material Simulation Kit for studying crystal plasticity using an FE based or a spectral numerical solver. Seminar of the Centro Nacional de Investigaciones Metalúrgicas (CENIM) del CSIC , Madrid, Spain (2015)
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…