Vatti, A. K.; Todorova, M.; Neugebauer, J.: Ab Initio Determined Phase Diagram of Clean and Solvated Muscovite Mica Surfaces. Langmuir 32 (4), pp. 1027 - 1033 (2016)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Ab initio Determination of Formation Energies and Charge Transfer Levels of Charged Ions in Water. APS 2016, Baltimore, MD, USA (2016)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Formation Energy of Ions in Water using ab-initio Molecular Dynamics. DPG Frühjahrstagung 2016, Regensburg, Germany (2016)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Formation Energy of Halide ions (Cl/Br/I) in water from ab-initio Molecular Dyna. Psi-k 2015 Conference, San Sebastián, Spain (2015)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Formation Energy of ions in water: An ab initio molecular dynamics study. 2nd German-Austrian Workshop on "Computational Materials Science on Complex Energy Landscapes", Kirchdorf, Austria (2015)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Modelling Mica from first-principles. 1st Dutch/German Workshop on Computational Materials Design, Balk, The Netherlands (2013)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Formation Energy of Zn-ions in water: An ab initio molecular dynamics study. ICMR Workshop - Workshop on Charged Systems and Solid/Liquid Interfaces, University of California , Santa Barbara, USA (2015)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Formation Energy of Zn-ions in water: An ab initio molecular dynamics study. ICMR Workshop - Advances in oxide materials: Preparation, properties, performance, University of California, Santa Barbara, CA, USA (2014)
Vatti, A. K.: An ab initio study of muscovite mica and formation energy of ions in liquid water. Dissertation, Fakultät für Maschinenbau der Ruhr-Universität Bochum, Bochum, Germany (2016)
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.