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)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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