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)
Hickel, T.; Aydin, U.; Sözen, H. I.; Dutta, B.; Pei, Z.; Neugebauer, J.: Innovative concepts in materials design to boost renewable energies. Seminar of Institute for Innovative Technologies, SRH Berlin University of Applied Sciences, Berlin, Germany (2020)
Janßen, J.; Hickel, T.; Neugebauer, J.: Automated ab-initio Determination of Materials Properties at finite Temperatures with pyiron. CNLS Seminar, Los Alamos, NM, USA (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)
Tehranchi, A.; Hickel, T.; Neugebauer, J.: Atomistic simulations of hydrogen-defect interactions in metals. Workshop "Hydrogen in Metals - current understanding and future needs", St Anne's College, Oxford, UK (2019)
Neugebauer, J.; Todorova, M.; Grabowski, B.; Hickel, T.: Modelling structural materials in realistic environments by ab initio thermodynamics. Hume-Rothery Award Symposium, TMS2019 Annual Meeting and Exhibition, San Antonio, TX, USA (2019)
Hickel, T.: Application of Density Functional Theory in the Context of Phase Diagram Modelling. MSIT Winter School on Materials Chemistry, Castle Ringberg, Tegernsee (2019)
Hickel, T.; Zendegani, A.; Körmann, F.; Neugebauer, J.: Energetics of non-stoichiometric stacking faults in Fe–Nb alloys: An ab initio study. TMS 2019 Annual Meeting, San Antonio, TX, USA (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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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.