Dick, A.; Körmann, F.; Hickel, T.; Neugebauer, J.: Ab initio based determination of thermodynamic properties of cementite including vibronic, magnetic and electronic excitations. Physical Review B 84 (12), 125101 (2011)
Körmann, F.; Dick, A.; Hickel, T.; Neugebauer, J.: Role of spin quantization in determining the thermodynamic properties of magnetic transition metals. Physical Review B 83 (16), 165114 (2011)
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)
Körmann, F.; Dick, A.; Hickel, T.; Neugebauer, J.: Rescaled Monte Carlo approach for magnetic systems: Ab initio thermodynamics of bcc iron. Physical Review B 81 (13), pp. 134425 - 134434 (2010)
von Pezold, J.; Dick, A.; Friák, M.; Neugebauer, J.: Generation and performance of special quasirandom structures for studying the elastic properties of random alloys: Application to Al–Ti. Physical Review B 81 (9), pp. 094203-1 - 094203-7 (2010)
Dick, A.; Hickel, T.; Neugebauer, J.: The Effect of Disorder on the Concentration-Dependence of Stacking Fault Energies in Fe1-xMnx – A First Principles Study. Steel Research International 80 (9), pp. 603 - 608 (2009)
Körmann, F.; Dick, A.; Hickel, T.; Neugebauer, J.: Pressure dependence of the Curie temperature in bcc iron studied by ab initio simulations. Physical Review B 79, 184406, pp. 184406-1 - 184406-5 (2009)
Körmann, F.; Dick, A.; Grabowski, B.; Hallstedt, B.; Hickel, T.; Neugebauer, J.: Free energy of bcc iron: Integrated ab initio derivation of vibrational, electronic, and magnetic contributions. Physical Review B 78, 033102 (2008)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
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.