Neugebauer, J.: Fundamental compositional limitations in the thin film growth of metastable alloys. 3rd Conference on Advanced Functional Materials (AFM2018), Vildmarkshotellet Kolmården, Norrköping, Sweden (2018)
Neugebauer, J.: Modelling thermodynamics and kinetics of general grain boundaries: Challenges and successes. Thermec 2018 Conference, Paris, France (2018)
Neugebauer, J.: First-principles approaches for charged defects in low dimensional systems. Conference on Physics of Defects in Solids, Trieste, Italy (2018)
Neugebauer, J.: Understanding fundamental doping and stoichiometry limits in semiconductors by ab initio modelling. EDS 2018 Conference, Thessaloniki, Greece (2018)
Zhu, L.-F.; Grabowski, B.; Neugebauer, J.: Efficient approach to compute melting properties fully from ab initio with application to Cu. CALPHAD XLVII Conference, Querétaro, México (2018)
Neugebauer, J.: Machine learning as tool to enhance ab initio based alloy design. Workshop: “Machine learning and data analytics in advanced metals processing", Manchester, UK (2018)
Neugebauer, J.: From electrons to the design of structurally complex materials. SFB ViCoM conference EPT 2018: From electrons to phase transitions, Vienna, Austria (2018)
Neugebauer, J.: Exploration of Large Ab Initio Data Spaces to Design Structural Materials with Superior Mechanical Properties. Hume-Rothery Award Symposium, TMS 2018, Phoenix, AZ, USA (2018)
Neugebauer, J.: Understanding the fundamental mechanisms behind H embrittlement: An ab initio guided multiscale approach. Seminar E2M ("Wall Forum") at MPI for Plasma Physics, Garching, Germany (2018)
Neugebauer, J.: A first principles approach to model electrochemical reactions in an electrolytic cell. Workshop: The Electrode Potential in Electrochemistry - A Challenge for Electronic Structure Theory Calculations, Schloß Reisensburg, Günzburg, Germany (2017)
Dutta, B.; Körmann, F.; Hickel, T.; Neugebauer, J.: Temperature-driven effects in functional materials: Ab initio insights. Talk at University Pierre and Marie CURIE (UPMC), Paris, France (2017)
Neugebauer, J.: Free energy sampling strategies for structurally complex materials. Workshop II: Stochastic Sampling and Accelerated Time Dynamics on Multidimensional Surfaces, IPAM, UCLA, Los Angeles, CA, USA (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
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.
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…