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)
Todorova, M.; Neugebauer, J.: Connecting semiconductor defect chemistry with electrochemistry: Impact of the electrolyte on the formation and concentration of point defects in ZnO. Surface Science 631, pp. 190 - 195 (2015)
Todorova, M.; Neugebauer, J.: Extending the concept of defect chemistry from semiconductor physics to electrochemistry. Physical Review Applied 1 (1), 014001 (2014)
Soon, A.; Wong, L.; Lee, M.; Todorova, M.; Delley, B.; Stampfl, C.: Nitrogen adsorption and thin surface nitrides on Cu(111) from first-principles. Surface Science 601, pp. 4775 - 4785 (2007)
Kenmoe, S.; Todorova, M.; Biedermann, P. U.; Neugebauer, J.: Impact of the vapour pressure of water on the equilibrium shape of ZnO nanoparticles: An ab-initio study. In APS March Meeting 2014, abstract #Q2.009. APS March Meeting 2014 , Denver, CO, USA, March 03, 2014 - March 07, 2014. (2014)
Kenmoe, S.; Todorova, M.; Biedermann, P. U.; Neugebauer, J.: Impact of the vapour pressure of water on the equilibrium shape of ZnO nanoparticles: An ab-initio study. In DPG Spring Meeting 2014, Abstract: O50.6. DPG Spring Meeting 2014 , Dresden, Germany, March 30, 2014 - April 04, 2015. (2014)
Todorova, M.; Surendralal, S.; Deißenbeck, F.; Wippermann, S. M.; Neugebauer, J.: Atomic insights into fundamental processes at electrochemical solid/liquid interface by ab initio calculations. 38th Topical Meeting of the International Society of Electrochemistry: Nanomaterials in Electrochemistry, Manchester, UK (2024)
Todorova, M.: Future directions in materials from modelling. Future directions in materials research in Europe organised by the Materials Australia VIC-TAS Branch/RMIT Europe, Online (2024)
Todorova, M.; Surendralal, S.; Deißenbeck, F.; Wippermann, S. M.; Neugebauer, J.: Ab Initio Calculations for electrified solid/liquid interfaces – Challenges, insights and Opportunities. GRC Aqueous Corrosion: Corrosion Challenges and Opportunities for the Energy Transition, New London, NH, USA (2024)
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
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.