Diehl, M.; Eisenlohr, P.; Shanthraj, P.; Roters, F.: Using the Spectral Solver. 5th International Symposium on Computational Mechanics of Polycrystals, CMCn 2016 and first DAMASK User Meeting, Düsseldorf, Germany (2016)
Mianroodi, J. R.; Shanthraj, P.; Svendsen, B.: Strongly versus weakly non-local dislocation transport and pile-up. 24th International Congress of Theoretical and Applied Mechanics, Montreal, Canada (2016)
Mianroodi, J. R.; Shanthraj, P.; Svendsen, B.: Comparison of algorithms and solution methods for classic and phase-field-based periodic inhomogeneous elastostatics. ECCOMAS Congress 2016, Crete, Greece (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)
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
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.
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…