Johansen, M.; Singh, M. P.; Gault, B.; Liu, F.: Suppressing Lithium Migration in a Carbon Fiber Negative Electrode During Atom Probe Tomography Analysis. Microscopy and Microanalysis 30 (6), pp. 1066 - 1073 (2024)
Johansen, M.; Singh, M. P.; Xu, J.; Asp, L. E.; Gault, B.; Liu, F.: Unravelling lithium distribution in carbon fibre electrodes for structural batteries with atom probe tomography. Carbon 225, 119091 (2024)
Woods, E.; Singh, M. P.; Kim, S.-H.; Schwarz, T.; Douglas, J. O.; El-Zoka, A.; Giulani, F.; Gault, B.: A versatile and reproducible cryo-sample preparation methodology for atom probe studies. Microscopy and Microanalysis, ozad120 29 (6), pp. 1992 - 2003 (2023)
Singh, M. P.; Woods, E.; Kim, S.-H.; Jung, C.; Aota, L. S.; Gault, B.: Facilitating the Systematic Nanoscale Study of Battery Materials by Atom Probe Tomography through in-situ Metal Coating. Batteries & Supercaps 7 (2), e202300403 (2023)
Woods, E.; Aota, L. S.; Schwarz, T.; Kim, S.-H.; Douglas, J. O.; Singh, M. P.; Gault, B.: In-situ cryogenic protective layers and metal coatings in cryogenic FIB. IMC20 - 20th International Microscopy Congress - Pre-congress workshop, Cryogenic Atom Probe Tomography, Busan, South Korea (2023)
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…