Dutta, B.; Hickel, T.; Neugebauer, J.: Ab initio modelling of phase diagrams in magnetic Heusler alloys: achievements and future challenges. SUSTech Global Scientists Forum, Shenzhen, China (2017)
Neugebauer, J.: Solvent-controlled single atom dissolution, surface alloying and Wulff shapes of nanoclusters; Electrocatalysis at electrocodes in the dry. Workshop: Research Area III, ZEMOS, Bochum, Germany (2016)
Neugebauer, J.: Collective variable description of crystal anharmonicity. IPAM Workshop II: Collective Variables in Classical Mechanics, Los Angeles, CA, USA (2016)
Neugebauer, J.: Modelling structural materials in extreme environments by ab initio guided multiscale simulations. International Workshop “Theory and Modelling of Materials in Extreme Environment", Abingdon, UK (2016)
Neugebauer, J.: Ab initio thermodynamic description of advanced structural materials: Status and challenges. Workshop “Ab-initio Based Modeling of Advanced Materials”, Yekaterinburg, Russia (2016)
Neugebauer, J.: Stahl: Wie ein alter Werkstoff sich immer wieder neu erfindet und damit Wissenschaft und Wirtschaft beflügelt. 129. Versammlung der Gesellschaft der deutschen Naturforscher und Ärzte, Greifswald, Germany (2016)
Dutta, B.; Hickel, T.; Neugebauer, J.: Intermartensitic Phase Boundaries in Ni–Mn–Ga Alloys: A Viewpoint from Ab initio Thermodynamics. 5th International Conference on Ferromagnetic Shape Memory Alloys, Sendai, Japan (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
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.
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…