Digitalization and Machine Learning

The digitalization of materials science and engineering is currently addressed with high priority worldwide. The FAIR handling of data, i.e. making them findable, accessible, interoperable and reusable, is also within the CM department a major driving force for novel digitial concepts and software developments. The employment of the integrated development pyiron is a central activity in this regard. The activities of the CM department are embedded in large-scale collaborative initiatives like Plattform MaterialDigital and NFDI-MatWerk.

Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron. [more]
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.  [more]
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. [more]
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