Kühbach, M. T.; Kasemer, M.; Gault, B.; Breen, A. J.: Open and strong-scaling tools for atom-probe crystallography: high-throughput methods for indexing crystal structure and orientation. Journal of Applied Crystallography 54 (Pt 5), pp. 1490 - 1508 (2021)
Kühbach, M. T.; London, A. J.; Wang, Jing, J.; Schreiber, D. K.; Mendez Martin, F.; Ghamarian, I.; Bilal, H.; Ceguerra, A. V.: Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy. Microscopy and Microanalysis, pp. 1 - 16 (2021)
Kühbach, M. T.; Roters, F.: Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations. Modelling and Simulation in Materials Science and Engineering 28, 055005 (2020)
Diehl, M.; Kühbach, M.: Coupled experimental-computational analysis of primary static recrystallization in low carbon steel. Modelling and Simulation in Materials Science and Engineering 28 (1), 014001 (2019)
Kühbach, M.; Breen, A. J.; Herbig, M.; Gault, B.: Building a Library of Simulated Atom Probe Data for Different Crystal Structures and Tip Orientations Using TAPSim. Microscopy and Microanalysis 25 (2), pp. 320 - 330 (2019)
Imran, M.; Kühbach, M.; Roters, F.; Bambach, M.: Development of a Model for Dynamic Recrystallization Consistent with the Second Derivative Criterion. Materials 10 (11), 1259, pp. 1 - 18 (2017)
Diehl, M.; Kühbach, M.; Kertsch, L.; Traka, K.; Raabe, D.: Coupled Experimental–Computational Analysis of Primary Static Recrystallization in Low Carbon Steel. Seminar of the Department of Mechanical Science and Engineering of the University of Illinois, Urbana-Champaign, Il, USA (2019)
Diehl, M.; Kühbach, M.; Raabe, D.: Experimental–computational analysis of primary static recrystallizazion in DC04 steel. 9th International Conference on Multiscale Materials Modeling , Osaka, Japan (2018)
Diehl, M.; Kühbach, M.; Raabe, D.: Experimental–computational analysis of primary static recrystallizazion in DC04 steel. 9th International Conference on Multiscale Materials Modeling , Osaka, Japan (2018)
Kühbach, M.; Breen, A. J.; Herbig, M.; Gault, B.; Raabe, D.: Building a Library of Simulated Atom Probe Data for Different Crystal Structures and Pillar Orientations Using TAPSim. APT&M 2018 International Conference on Atom-Probe Tomography & Microscopy, Washington, DC, USA (2018)
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
The utilization of Kelvin Probe (KP) techniques for spatially resolved high sensitivity measurement of hydrogen has been a major break-through for our work on hydrogen in materials. A relatively straight forward approach was hydrogen mapping for supporting research on hydrogen embrittlement that was successfully applied on different materials, and…
It is very challenging to simulate electron-transfer reactions under potential control within high-level electronic structure theory, e. g. to study electrochemical and electrocatalytic reaction mechanisms. We develop a novel method to sample the canonical NVTΦ or NpTΦ ensemble at constant electrode potential in ab initio molecular dynamics…
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…
Photovoltaic materials have seen rapid development in the past decades, propelling the global transition towards a sustainable and CO2-free economy. Storing the day-time energy for night-time usage has become a major challenge to integrate sizeable solar farms into the electrical grid. Developing technologies to convert solar energy directly into…
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…