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)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.