Mitra, C.; Lange, B.; Freysoldt, C.: Quasiparticle band offsets of semiconductor heterojunctions from a generalized marker method. Physical Review B 84 (19), 193304, pp. 1 - 4 (2011)
Lange, B.; Freysoldt, C.; Neugebauer, J.: Native and hydrogen-containing point defects in Mg3N2: A density functional theory study. Physical Review B 81, 224109, pp. 1 - 10 (2010)
Lange, B.; Freysoldt, C.; Neugebauer, J.: Point-defect energetics from LDA, PBE, and HSE: Different functionals, different energetics? 1.st Austrian/German Workshop on Computational Materials Design, Kramsach, Tyrol, Austria (2012)
Lange, B.; Freysoldt, C.; Neugebauer, J.: Highly p-doped GaN:Mg! What hinders the thermal drive-out of hydrogen? 2. Klausurtagung des Graduierten Kollegs: Mikro und Nanostrukturen in der Optoelektronik, Bad Karlshafen, Germany (2009)
Lange, B.; Freysoldt, C.; Neugebauer, J.: Role of the parasitic Mg3N2 phase in post-growth activation of p-doped Mg:GaN. DPG Frühjahrstagung, TU Dresden, Germany (2009)
Lange, B.; Freysoldt, C.; Neugebauer, J.: Role of the parasitic Mg3N2 phase in post-groth activation of p-doped Mg:GaN. ICNS-8, Jeju Island, South Korea (2009)
Lange, B.; Freysoldt, C.; Neugebauer, J.: Role of the parasitic Mg3N2 phase in post-growth activation of p-doped Mg:GaN. CECAM Workshop 09: Which Electronic Structure Method for the Study of Defects?, CECAM-HQ-EPFL, Lausanne, Switzerland (2009)
Lange, B.: Limitierungen der p-Dotierbarkeit von Galliumnitrid: Eine Defektstudie von GaN:Mg auf Basis der Dichtefunktionaltheorie. Dissertation, Universität Paderborn, Paderborn, Germany (2012)
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.