Ma, D.; Friák, M.; von Pezold, J.; Raabe, D.; Neugebauer, J.: Computationally efficient and quantitatively accurate multiscale simulation of solid-solution strengthening by ab initio calculation. Acta Materialia 85, pp. 53 - 66 (2015)
Todorova, M.; Neugebauer, J.: Connecting semiconductor defect chemistry with electrochemistry: Impact of the electrolyte on the formation and concentration of point defects in ZnO. Surface Science 631, pp. 190 - 195 (2015)
Albrecht, M.; Lymperakis, L.; Neugebauer, J.: Origin of the unusually strong luminescence of a-type screw dislocations in GaN. Physical Review B 90 (24), 241201 (2014)
Dutta, B.; Hickel, T.; Entel, P.; Neugebauer, J.: Ab Initio Predicted Impact of Pt on Phase Stabilities in Ni–Mn–Ga Heusler alloys. Journal of Phase Equilibra and Diffusion 35 (6), pp. 695 - 700 (2014)
Race, C.; von Pezold, J.; Neugebauer, J.: Role of the mesoscale in migration kinetics of flat grain boundaries. Physical Review B 89 (21), 214110 (2014)
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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
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.