Diehl, M.; Shanthraj, P.; Eisenlohr, P.; Roters, F.: Neighborhood influences on stress and strain partitioning in dual-phase microstructures. An investigation on synthetic polycrystals with a robust spectral-based numerical method. Meccanica 51 (2), pp. 429 - 441 (2016)
Friák, M.; Tytko, D.; Holec, D.; Choi, P.-P.; Eisenlohr, P.; Raabe, D.; Neugebauer, J.: Synergy of atom-probe structural data and quantum-mechanical calculations in a theory-guided design of extreme-stiffness superlattices containing metastable phases. New Journal of Physics 17 (9), 093004 (2015)
Tjahjanto, D. D.; Eisenlohr, P.; Roters, F.: Multiscale deep drawing analysis of dual-phase steels using grain cluster-based RGC scheme. Modelling and Simulation in Materials Science and Engineering 23 (4), 045005 (2015)
Shanthraj, P.; Eisenlohr, P.; Diehl, M.; Roters, F.: Numerically robust spectral methods for crystal plasticity simulations of heterogeneous materials. International Journal of Plasticity 66, pp. 31 - 45 (2015)
Reuber, J. C.; Eisenlohr, P.; Roters, F.; Raabe, D.: Dislocation density distribution around an indent in single-crystalline nickel: Comparing nonlocal crystal plasticity finite-element predictions with experiments. Acta Materialia 71, pp. 333 - 348 (2014)
Blum, W.; Dvořák, J.; Král, P. T. K.; Eisenlohr, P.; Sklenička, V.: Effect of grain refinement by ECAP on creep of pure Cu. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 590, pp. 423 - 432 (2014)
Eisenlohr, P.; Diehl, M.; Lebensohn, R. A.; Roters, F.: A spectral method solution to crystal elasto-viscoplasticity at finite strains. International Journal of Plasticity 46, pp. 37 - 53 (2013)
Wang, L.; Barabash, R.; Bieler, T.; Liu, W.; Eisenlohr, P.: Study of {1121} Twinning in alpha-Ti by EBSD and Laue Microdiffraction. Metallurgical and Materials Transactions A 44 (8), pp. 3664 - 3674 (2013)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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