Kovács, A.; Pradeep, K. G.; Herzer, G.; Raabe, D.; Dunin-Borkowski, R. E.: Magnetic microstructure in a stress-annealed Fe73.5Si15.5B7Nb3Cu1 soft magnetic alloy observed using off-axis electron holography and Lorentz microscopy. AIP Advances 6 (5), 056501 (2016)
Pradeep, K. G.; Herzer, G.; Raabe, D.: Atomic scale study of CU clustering and pseudo-homogeneous Fe-Si nanocrystallization in soft magnetic FeSiNbB(CU) alloys. Ultramicroscopy 159 (2), pp. 285 - 291 (2015)
Pradeep, K. G.; Tasan, C. C.; Yao, M.; Deng, Y.; Springer, H.; Raabe, D.: Non-equiatomic high entropy alloys: Approach towards rapid alloy screening and property-oriented design. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 648, pp. 183 - 192 (2015)
Zhang, H.; Pradeep, K. G.; Mandal, S.; Ponge, D.; Raabe, D.: New insights into the austenitization process of low-alloyed hypereutectoid steels: Nucleation analysis of strain-induced austenite formation. Acta Materialia 80, pp. 296 - 308 (2014)
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 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…