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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.