Gault, B.; Kwiatkowski da Silva, A.; Zhao, H.; Ponge, D.; Raabe, D.: Atom probe tomography for studying the interplay of segregation and phase transformations. 17th International Conference on Aluminium Alloys, online, Grenoble, France (2020)
Gault, B.: An introduction to atom probe tomography: from fundamentals to atomic-scale insights into engineering materials. Rolls Royce Lunch Time Seminar, Derby, UK (2020)
Kwiatkowski da Silva, A.; Ponge, D.; Gault, B.; Raabe, D.: The Relevance of Interfacial Segregation for Controlling Second Phase Precipitation in Advanced High Strength Steels. TMS 2020 Annual Meeting & Exhibition, San Diego, CA, USA (2020)
Gault, B.: Can machine learning bring atom probe microscopy closer to analytical atomic-scale tomography. 12th International Symposium on Atomic Level Characterizations for New Materials and Devices (ALC 19), Kyoto, Japan (2019)
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.
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…
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.