Titrian, H.; Aydin, U.; Friák, M.; Ma, D.; Raabe, D.; Neugebauer, J.: Self-consistent scale-bridging approach to compute the elasticity of multi-phase polycrystalline materials. Materials Research Society Symposia Proceedings 1524, pp. 17 - 23 (2013)
Holec, D.; Friák, M.; Neugebauer, J.; Mayrhofer, P. H.: Trends in the elastic response of binary early transition metal nitrides. Physical Review B 85, pp. 064101-1 - 064101-9 (2012)
Holec, D.; Friák, M.; Dlouhy, A.; Neugebauer, J.: Ab initio study of pressure stabilized NiTi allotropes: Pressure-induced transformations and hysteresis loops. Physical Review B 84, pp. 224119-1 - 224119-8 (2011)
Zelený, M.; Friák, M.; Šob, M.: Ab initio study of energetics and magnetism of Fe, Co, and Ni along the trigonal deformation path. Physical Review B 83, pp. 184424-1 - 184424-7 (2011)
Counts, W. A.; Friák, M.; Raabe, D.; Neugebauer, J.: Using ab initio calculations in designing bcc MgLi–X alloys for ultra-lightweight applications. Advanced Engineering Materials 12 (12), pp. 1198 - 1205 (2010)
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…