Eisenlohr, P.: Einheitliche Beschreibung dynamischer und statischer Erholung von Stufenversetzungen mittels Dipolweitenverteilungen. Seminar of the Institute of Materials Physics, University of Vienna, Vienna, Austria (2003)
Reuber, J. C.; Eisenlohr, P.; Roters, F.: Boundary Layer Formation in Continuum Dislocation Dynamics. Dislocations 2016, Purdue University, West Lafayette, IN, USA (2016)
Shanthraj, P.; Diehl, M.; Eisenlohr, P.; Roters, F.: Numerically robust spectral methods for crystal plasticity simulations of heterogeneous materials. Materials to Innovate Industry and Society, Noordwijkerhout, The Netherlands (2013)
Diehl, M.; Eisenlohr, P.; Roters, F.; Raabe, D.: Using a "Virtual Laboratory" to Derive Mechanical Properties of Complex Microstructures. 11th GAMM-Seminar on Microstructures, Essen, Germany (2012)
Diehl, M.; Eisenlohr, P.; Roters, F.; Tasan, C. C.; Raabe, D.: Using a "Virtual Laboratory" to Derive Mechanical Properties of Complex Microstructures. Materials to Innovate Industry and Society, Noordwijkerhout, The Netherlands (2011)
Kords, C.; Eisenlohr, P.; Roters, F.: Signed dislocation densities and their spatial gradients as basis for a nonlocal crystal plasticity model. MMM 2010 Fifth International Conference Multiscale Materials Modeling, Freiburg, Germany (2010)
Kords, C.; Eisenlohr, P.; Roters, F.: A Non-Local Dislocation Density Based Constitutive Model for Crystal Plasticity. Junior Euromat 2010, Lausanne, Switzerland (2010)
Eisenlohr, P.: On the role of dislocation dipoles in unidirectional deformation of crystals. Dissertation, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen (2004)
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…