Krüger, T.; Varnik, F.; Raabe, D.: Second-order convergence of the deviatoric stress tensor in the standard Bhatnagar-Gross-Krook lattice Boltzmann method. Physical Review E 82 (025701) (2010)
Ayodele, S. G.; Varnik, F.; Raabe, D.: Effect of aspect ratio on transverse diffusive broadening: A lattice Boltzmann study. Physical Review E 80 (1), pp. 016304-1 - 016304-9 (2009)
Ayodele, S. G.; Varnik, F.; Raabe, D.: Transverse diffusive mixing of solutes in pressure driven microchannels: A Lattice Boltzmann study of the scaling laws. La Houille Blanche, International Water Journal 6, pp. 93 - 100 (2009)
Gross, M.; Varnik, F.; Raabe, D.: Fall and rise of small droplets on rough hydrophobic substrates. Europhysics Letters 88 (26002), pp. 26002-p1 - 26002-p6 (2009)
Varnik, F.; Raabe, D.: Scaling effects in microscale fluid flows at rough solid surfaces. Modeling and Simulation in Materials Science and Engineering 14, pp. 857 - 873 (2006)
Baschnagel, J.; Varnik, F.: Computer simulations of supercooled polymer melts in the bulk and in confined geometry. Journal of Physics: Condensed Matter 17 (32), pp. R851 - R953 (2005)
Varnik, F.; Bocquet, L.; Barrat, L.-J.: A study of the static yield stress in a binary Lennard-Jones glass. The Journal of Chemical Physics 120 (6), pp. 2788 - 2801 (2004)
Baschnagel, J.; Meyer, H.; Varnik, F.; Metzger, S.; Aichele, M.; Müller, M.; Binder, K.: Computer Simulations of Polymers close to Solid Interfaces: Some Selected Topics. Special Issue of Interface Science: Polymers at Interfaces 11, pp. 159 - 173 (2003)
Varnik, F.; Baschnagel, J.; Binder, K.; Mareschal, M.: Confinement effects on the slow dynamics of a supercooled polymer melt: Rouse modes and the incoherent scattering function. European Physical Journal E 12 (167) (2003)
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
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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…