Makineni, S. K.; Raabe, D.; Gault, B.: Development of high temperature Mo–Si–B based alloy through Laser Additive Manufacturing. Intermetallics 2017, Bad Staffelstein, Germany (2017)
Rusitzka, A. K.; Stephenson, L.; Gremer, L.; Raabe, D.; Willbold, D.; Gault, B.: Getting insights to Alzheimer‘s disease by atom probe tomography. 6th International caesar conference, Overcoming Barriers — atomic-resolution and beyond: advances in molecular electron microscopy, Bonn, Germany (2017)
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
Magee, C. W.; Lutz, N.; Renelle, D.; Baptiste, G.; Simon, B.: Technical note: Investigation into the mechanism of chemical abrasion using SHRIMP, Raman spectroscopy and atom probe tomography. EGUSphere, pp. 1 - 23 (2025)
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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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.