Schmitt, M.; Spiegel, M.: High Temperature Corrosion: Corrosion process of stainless steels and nickel base alloys under BtE and WtE conditions. International Conference on Waste and Biomass Combustion, Michelangelo Hotel Milano, Italy (2008)
Schmitt, M.; Spiegel, M.: Interim report on corrosion data: Dependence on variation of chemical environment. NextGenBioWaste, 2nd Progress Meeting 2008, Schiphol Airport Amsterdam, The Netherlands (2008)
Schmitt, M.; Spiegel, M.: Contribution to the analysis of the corrosion process of metallic materials in incineration plants. EUROCORR 2008, EICC Edinburgh, UK (2008)
Schmitt, M.; Spiegel, M.: High Temperature Corrosion: Corrosion mechanism of candidate materials in lab-scale incineration environments. General Assembly NextGenBioWaste 2008, De Zwijger Amsterdam, The Netherlands (2008)
Schmitt, M.; Spiegel, M.: Corrosion and fouling data of candidate materials for WtE components: Part II. NextGenBioWaste, 1st Progress Meeting 2008, Schiphol Airport Amsterdam, The Netherlands (2008)
Schmitt, M.; Spiegel, M.: Corrosion and fouling data of candidate materials for WtE components: Part I. NextGenBioWaste, 2nd Progress Meeting 2007, Schiphol Airport Amsterdam, The Netherlands (2007)
Schmitt, M.; Spiegel, M.: Introduction to the Working Group NGBW. NextGenBioWaste, 1st Progress Meeting 2007, Schiphol Airport Amsterdam, The Netherlands (2007)
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 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.
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…