Sreekala, L.; Dey, P.; Hickel, T.; Neugebauer, J.: Unveiling nonmonotonic chemical trends in the solubility of H in complex Fe–Cr–Mn carbides by means of ab initio based approaches. Physical Review Materials 6 (1), 014403 (2022)
Hickel, T.; McEniry, E.; Nazarov, R.; Dey, P.: Ab initio basierte Simulation zur Wasserstoffversprödung in hoch-Mn Stählen. Seminar der Staatlichen Materialprüfungsanstalt Darmstadt, Institut für Werkstoffkunde, Darmstadt, Germany (2020)
Dey, P.: Materials design based on ab initio methods: Coherent microstructure & its impact on real application. Seminar at TU Delft, Delft, The Netherlands (2018)
Dey, P.; Yao, M.; Friák, M.; Hickel, T.; Raabe, D.; Neugebauer, J.: Ab-initio investigation of the role of kappa carbide in upgrading Fe–Mn–Al–C alloy to the class of advanced high-strength steels. ArcelorMittal Global R&D Gent, Thessaloniki, Greece (2017)
Dey, P.: Ab initio investigation of the interaction of hydrogen with carbides in advanced high-strength steels. Seminar at Southern University of Science and Technology, Shenzhen, China (2017)
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
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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.