Remmele, T.; Schulz, T.; Markurt, T.; Korytov, M.; Albrecht, M.; Duff, A.; Lymperakis, L.; Neugebauer, J.: Quantitative measurement of composition fluctuations in InGaN quantum wells. 15th European Microscopy Congress, Manchester Central, UK (2012)
Duff, A.; Lymperakis, L.; Neugebauer, J.: Ab-initio based comparitive study of In incorporation and surface segregation on III- and N-face {0001} InGaN surfaces. SINOPLE mid-term meeting, Berlin, Germany (2011)
Kalesaki, E.; Lymperakis, L.; Kioseoglou, J.; Komninou, P.; Karakostas, T.: Surface Thermodynamics of (11-22) and (11-2-2) Semipolar AlN Surfaces. International Workshop on Nitride Semiconductors, Tampa, FL, USA (2010)
von Pezold, J.; Lymperakis, L.; Neugebauer, J.: A multiscale study of the Hydrogen enhanced local plasticity (HELP) mechanism. IWoM3 2009 - International Workshop on Multiscale Materials Modeling, Berlin, Germany (2009)
Petrov, M.; Friák, M.; Lymperakis, L.; Neugebauer, J.; Raabe, D.: Hardness anisotropy of crystalline alpha-chitin: An ab-initio based conformational analysis. Spring meeting of the German Physical Society (DPG), Regensburg, Germany (2007)
Petrov, M.; Friák, M.; Lymperakis, L.; Neugebauer, J.; Raabe, D.: An ab-initio study of hardness anisotropy of crystalline alpha-chitin. International Max-Planck Workshop on Multiscale Modeling of Condensed Matter, Sant Feliu de Guixols, Spain (2007)
Lymperakis, L.; Neugebauer, J.: Exploring the 5D configurational space of grain boundaries in aluminun: An ab-initio based multiscale analysis. MRS Fall Meeting, Boston, MA, USA (2006)
Lymperakis, L.; Neugebauer, J.: Ab-initio based multiscale calculations of low-angle grain boundaries in Aluminium. Materials Research Society fall meeting, Boston, MA, USA (2005)
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…