Jenko, D.; Palm, M.: Transmission electron microscopy of the Fe–Al–Ti–B alloys with additions of Mo. 19th International Microscopy Congress (IMC19), Sidney, Australia (2018)
Prokopčáková, P.; Švec, M.; Lotfian, S.; Palm, M.: Microstructure – property relationships of iron aluminides. 64. Metallkunde-Kolloquium Montanuniversität Leoben, Lech am Arlberg, Austria (2018)
Peng, J.; Moszner, F.; Vogel, D.; Palm, M.: Influence of the Al content on the aqueous corrosion resistance of binary Fe–Al alloys in H2SO4. Intermetallics 2017, Educational Center Kloster Banz, Bad Staffelstein, Germany (2017)
Peng, J.; Vogel, D.; Palm, M.: Influence of the Al content on the corrosion resistance of binary Fe–Al alloys in H2SO4. EUROMAT 2017 – European Congress and Exhibition on Advanced Materials and Processes, Thessaloniki, Greece (2017)
Palm, M.: Development and processing of advanced iron aluminide alloys for application at high temperatures. 62. Metallkunde Kolloquium
, Lech am Arlberg, Austria (2016)
Marx, V. M.; Palm, M.: The wet and hot corrosion behavior of iron aluminides. THERMEC 2016 – Int. Conf. on Processing & Manufacturing of Advanced Materials
, Graz, Austria (2016)
Palm, M.: Iron aluminides: From alloy development to processing. The Materials Chain from Discovery to Production (contributed talk), Bochum, Germany (2016)
Hasemann, G.; Gang, F.; Palm, M.; Bogomol, I.; Krüger , M.: Determining the ternary eutectic alloy composition on the Mo-rich side of the Mo–Si–B system. Advances in Materials & Processing Technologies – AMPT 2015, Madrid, Spain (2015)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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.
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…