Cao, Y. P.; Ma, D.; Raabe, D.: The use of flat punch indentation to determine the viscoelastic properties in the time and frequency domains of a soft layer bonded to a rigid substrate. Acta Biomaterialia 5 (1), pp. 240 - 248 (2009)
Cao, Y. P.; Xue, Z. Y.; Chen, X.; Raabe, D.: Correlation between the flow stress and the nominal indentation hardness of soft metals. Scripta Materialia 59, pp. 518 - 521 (2008)
Cao, Y. P.: Determination of the creep exponent of a power-law creep solid using indentation tests. Mechanics of Time-Dependent Materials 11, pp. 159 - 173 (2007)
Balasundaram, K.; Cao, Y. P.; Raabe, D.: Investigating the Applicability of the Oliver & Pharr Method to the Nano-Mechanical Characterization of Soft Matter. Gerberich Symposium, 1st International Conference from Nanoparticles and Nanomaterials to Nanodevices and Nanosystems, Halkidiki, Greece (2008)
Balasundaram, K.; Cao, Y. P.; Raabe, D.: Nanomechanics characterization of softmatter using nanoindentation. 11th GLADD Meeting, TU Gent, Belgium (2008)
Balasundaram, K.; Cao, Y. P.; Raabe, D.: Nano-mechanical Characterization of Soft Matter. Materials science Day, Mechanical Engineering Department at Ruhr-University of Bochum, Bochum, Germany (2008)
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…