Kwiatkowski da Silva, A.; Ponge, D.; Inden, G.; Gault, B.; Raabe, D.: Physical Metallurgy of segregation, austenite reversion, carbide precipitation and related phenomena in medium Mn steels. Gordon Research Conference: Physical Metallurgy, Biddeford, ME, USA (2017)
Gault, B.: Graduate course on Atom Probe Tomography, as part of the Centre for Doctoral Training on Materials Charactisation. Lecture: SS 2024, Imperial College London, UK, 2024-04 - 2024-07
Gault, B.: Graduate course on Atom Probe Tomography, as part of the Centre for Doctoral Training on Materials Charactisation. Lecture: SS 2023, Imperial College London, UK, 2023-04 - 2023-07
Gault, B.: Graduate course on Atom Probe Tomography, as part of the Centre for Doctoral Training on Materials Charactisation. Lecture: SS 2022, Imperial College London, UK, 2022-04 - 2022-07
Gault, B.: Graduate course on Atom Probe Tomography, as part of the Centre for Doctoral Training on Materials Charactisation. Lecture: SS 2021, Imperial College London, UK, 2021-04 - 2021-07
Lee, C.-G.; Nallathambi, V.; Kang, T.; Aota, L. S.; Reichenberger, S.; El-Zoka, A.; Choi, P.-P.; Gault, B.; Kim, S.-H.: Magnetocaloric effect of Fe47.5Ni37.5Mn15 bulk and nanoparticles: A cost-efficient alloy for room temperature magnetic refrigeration. arXiv (2024)
Kim, S.-H.; Yoo, Su, S.-H.; Aota, L. S.; El-Zoka, A.; Kang, P. W.; Lee, Y.; Gault, B.: B dopant evolution in Pd catalysts after H evolution/oxidation reaction in alkaline environment. arXiv (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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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…