Hickel, T.; Uijttewaal, M.; Grabowski, B.; Lencer, D.; Neugebauer, J.: First principles determination of structural phase transitions in smart materials. International Workshop on Multiscale Materials Modelling (IWoM3), Berlin, Germany (2009)
Körmann, F.; Dick, A.; Grabowski, B.; Hickel, T.; Neugebauer, J.: The free energy of iron: Integrated ab initio derivation of vibrational, electronic, and magnetic contributions. International Workshop on Multiscale Materials Modelling (IWoM3), Berlin, Germany (2009)
Udyansky, A.; Friák, M.; Grabowski, B.; Hickel, T.; Neugebauer, J.: First Principles Study of Fe–C interstitial solid solutions. International Workshop on Ab initio Description of Iron and Steel (ADIS2008), Ringberg Castle, Germany (2008)
Hickel, T.; Uijttewaal, M.; Grabowski, B.; Neugebauer, J.: First principles determination of phase transitions: The (pre)martensitic transition in Ni2MnGa. UCSB-MPG Workshop on Inorganic Materials for Energy Conversion, Storage and Conservation, UCLA Lake Arrowhead Conference Center, CA, USA (2008)
Körmann, F.; Dick, A.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Importance of magnetism for the thermal expansion of transition metals: An ab initio study. Spring meeting of the German Physical Society (DPG), Berlin, Germany (2008)
Hickel, T.; Uijttewaal, M.; Grabowski, B.; Neugebauer, J.: A first principle determination of phase transitions in magnetic shape memory alloys. Multiscale approach to alloys: Advances and challenges, Stockholm, Sweden (2007)
Hickel, T.; Uijttewaal, M.; Grabowski, B.; Neugebauer, J.: Determination of symmetry reduced structures by a soft-phonon analysis in magnetic shape memory alloys. Theory meets industry. The impact of density-functional calculation on materials science, Vienna, Austria (2007)
Grabowski, B.; Hickel, T.; Neugebauer, J.: Accuracy and error bars of DFT calculated thermodynamic properties for elementary metals. 13th International Workshop on Computational Physics and Materials Science: Total Energy and Force Methods, Trieste, Italy (2007)
Hickel, T.; Grabowski, B.; Uijttewaal, M.; Neugebauer, J.: Determination of symmetry-reduced structures by a soft-phonon analysis in magnetic shape memory alloys. 13th International Workshop on Computational Physics and Materials Science: Total Energy and Force Methods, Trieste, Italy (2007)
Hickel, T.; Grabowski, B.; Neugebauer, J.; Neumann, B.; Neumann, K.-U.; Ziebeck, K. R. A.: Temperature dependent properties of the Heusler alloy Ni2+xMn1-xGa. International Workshop on Ab initio Description of Iron and Steel (ADIS2006), Status and future challenges, Ringberg Castle, Germany (2006)
Hickel, T.; Grabowski, B.; Neumann, K.; Neumann, K.-U.; Ziebeck, K. R. A.; Neugebauer, J.: Temperature dependent properties of Ni-rich Ni2MnGa. Materials Research Society fall meeting, Boston, MA, USA (2005)
Grabowski, B.: Towards ab initio assisted materials design: DFT based thermodynamics up to the melting point. Dissertation, University of Paderborn, Paderborn, Germany (2009)
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…