Abbasi, A.; Dick, A.; Hickel, T.; Neugebauer, J.: First-principles investigation of the effect of carbon on the stacking fault energy of Fe–C alloys. Acta Materialia 59, pp. 3041 - 3048 (2011)
Dick, A.; Körmann, F.; Abbasi, A.; Hickel, T.; Neugebauer, J.: Towards an ab initio based understanding of deformation mechanisms in high-manganese Steels. 1st Int. Conf. on High Manganese Steels, Seoul, South Korea (2011)
Abbasi, A.; Dick, A.; Hickel, T.; Neugebauer, J.: First principles calculations of the stacking fault energies for Mn and Fe. Computational Materials Science on Complex Energy Landscapes Workshop, Imst, Austria (2010)
Abbasi, A.; Dick, A.; Hickel, T.; Neugebauer, J.: The influence of interstitial carbon on the stacking fault energy of Fe based materials. Psi-k Conference 2010, Berlin, Germany (2010)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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.
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…