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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…