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)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
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.