Neugebauer, J.; Surendralal, S.; Todorova, M.: First-principles appraoch to model electrochemical reactions at solid-liquid interfaces. ACS 2019 Fall Meeting & Exhibition, San Diego, CA, USA (2019)
Todorova, M.; Surendralal, S.; Neugebauer, J.: Degradation processes at surfaces and interfaces. ISAM4: The fourth International Symposium on Atomistic and Multiscale Modeling of Mechanics and Multiphysics, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany (2019)
Neugebauer, J.; Huber, L.; Körmann, F.; Grabowski, B.; Hickel, T.: Ab initio input for multiphysics models: Accuracy, performance and challenges. ISAM4: The fourth International Symposium on Atomistic and Multiscale Modeling of Mechanics and Multiphysics, Erlangen, Germany (2019)
Neugebauer, J.: Machine Learning in Materials: Screening and Discovery. Gordon Research Conference Physical Metallurgy „Coupling Computation, Data Science and Experiments in Physical Metallurgy“, Manchester, NH, USA (2019)
Todorova, M.; Surendralal, S.; Neugebauer, J.: Building an ab-initio potentiostat in a standard DFT code with periodic boundary conditions. ELRC2019 - IPAM reuniuon workshop, Lake Arrowhead, CA, USA (2019)
Zhu, L.-F.; Janßen, J.; Grabowski, B.; Neugebauer, J.: Melting parameters from ab initio using the fast statistical sampling TOR-TILD approach: Applications to Al and Ni. CALPHAD XLVIII CONFERENCE, Singapore, Singapore (2019)
Todorova, M.; Yoo, S.-H.; Surendralal, S.; Neugebauer, J.: A fully ab initio approach to modelling electrochemical solid/liquid interfaces. Chemiekolloquium der Johannes Kepler Universität Linz, Linz, Austria (2019)
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.