Zhu, L.-F.; Neugebauer, J.; Grabowski, B.: Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential. CALPHAD L Conference, Cambridge, MA, USA (2023)
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)
Zhu, L.-F.; Janßen, J.; Grabowski, B.; Neugebauer, J.: Melting parameters from ab initio using the fast statistical sampling TOR-TILD approach: Applications to Al and Ni. CALPHAD XLVIII CONFERENCE, Singapore, Singapore (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)
Neugebauer, J.; Janßen, J.; Körmann, F.; Hickel, T.; Grabowski, B.: Exploration of large ab initio data spaces to design materials with superior mechanical properties. Physics and Theoretical Division Colloquium, Los Alamos, NM, USA (2019)
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)
Grabowski, B.: Knowledge driven engineering of materials: Development and application of ab initio based scale bridging methods. Seminar at HSU Hamburg, Hamburg, Germany (2018)
Grabowski, B.: Efficient and Accurate Computation of Melting Temperatures and Enthalpies and Entropies of Fusion from Ab Initio. TMS conference, Phoenix, AZ, USA (2018)
Grabowski, B.: Knowledge driven engineering of materials: Development and application of ab initio based scale bridging methods. Seminar at University Stuttgart, Stuttgart, Germany (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
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…