Glensk, A.; Grabowski, B.; Hickel, T.; Neugebauer, J.: CALPHAD assessments using T > 0K ab initio data: From quasiharmonic to local anharmonic approximation. CALPHAD 2015, Loano, Italy (2015)
Opahle, I.; Madsen, G. K. H.; Dorigo, M.; Bera, C.; Glensk, A.; Drautz, R.: High-throughput density functional screening of thermoelectric materials. Evaluation ICAMS 2013, Bochum, Germany (2013)
Glensk, A.: Anharmonic contributions to ab initio computed thermodynamic material properties. Dissertation, University of Paderborn, Paderborn, Germany (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
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.