Kim, S.-H.; Shin, K.; Zhou, X.; Jung, C.; Kim, H. Y.; Pedrazzini, S.; Conroy, M.; Henkelman, G.; Gault, B.: Atom probe analysis of BaTiO3 enabled by metallic shielding. Scripta Materialia 229, 115370 (2023)
Aota, L. S.; Jung, C.; Zhang, S.; Kim, S.-H.; Gault, B.: Revealing Compositional Evolution of PdAu Electrocatalyst by Atom Probe Tomography. ACS Energy Letters 8 (6), pp. 2824 - 2830 (2023)
Dubosq, R.; Woods, E.; Gault, B.; Best, J. P.: Electron microscope loading and in situ nanoindentation of water ice at cryogenic temperatures. PLoS One 18 (2), e0281703 (2023)
Kim, S.-H.; Jun, H.; Jang, K.; Choi, P.-P.; Gault, B.; Jung, C.: Exploring the Surface Segregation of Rh Dopants in PtNi Nanoparticles through Atom Probe Tomography Analysis. The Journal of Physical Chemistry C 127 (46), pp. 22721 - 22725 (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)
Zhu, Y.; Heo, T. W.; Rodriguez, J. N.; Weber, P. K.; Shi, R.; Baer, B. J.; Morgado, F. F.; Antonov, S.; Kweon, K. E.; Watkins, E. B.et al.; Savage, D. J.; Chapman, J. E.; Keilbart, N. D.; Song, Y.; Zhen, Q.; Gault, B.; Vogel, S. C.; Sen-Britain, S. T.; Shalloo, M. G.; Orme, C.; Bagge-Hansen, M.; Hahn, C.; Pham, T. A.; Macdonald, D. D.; Qiu, R. S.; Wood, B. C.: Hydriding of titanium: Recent trends and perspectives in advanced characterization and multiscale modeling. Current Opinion in Solid State and Materials Science 26, 101020 (2022)
Saksena, A.; Kubacka, D.; Gault, B.; Spieker, E.; Kontis, P.: The effect of γ matrix channel width on the compositional evolution in a multi-component nickel-based superalloy. Scripta Materialia 219, 114853 (2022)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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…
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.