Bhat, M. K.; Brink, T.; Ding, H.; Jung, C.; Best, J. P.; Dehm, G.: Influence of the Structure and Chemistry of Σ5 Grain Boundaries on Microscale Strengthening in Cu Bicrystals. TMS Annual Meeting and Exhibition 2024, Orlando, FL, USA (2024)
Brink, T.; Bhat, M. K.; Best, J. P.; Dehm, G.: Grain-boundary segregation effects on bicrystal Cu pillar compression. DPG Spring Meeting, Dresden, Germany (2023)
Bhat, M. K.; Frommeyer, L.; Prithiv, T. S.; Dehm, G.; Best, J. P.: Using small-scale mechanics to probe the origins of segregation-induced strengthening. Nanomechanical Testing in Materials Research and Development VIII, Split, Croatia (2022)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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…