Polin, N.; Giron, S.; Adabifiroozjaei, E.; Yang, Y.; Saxena, A.; Gutfleisch, O.; Gault, B.: Atomic‐scale insights to design of high‐performing SmCo based sintered permanent magnets gained by atom probe tomography. 12th International Conference on Magnetic and Superconducting Materials (MSM22), Duisburg, Germany (2022)
Gault, B.: Pushing the analytical limits of atom probe tomography via cryo-enabled workflows. Microscience Microscopy Congress 2021, online, Oxford, UK (2021)
Gault, B.; Guillon, O.: Du térawatt au picomètre: Voyage au cœur des technologies de l’hydrogène. Café des Sciences de l’Ambassade de France en Allemagne, online, Berlin, Germany (2021)
Gault, B.: Advancing corrosion understanding with (cryo-) Atom Probe Tomography. Imperial College London - Rolls Royce corrosion seminar, online, London, UK (2021)
Gault, B.: Machine-Learning for Atom Probe Tomography. Workshop 'Research-data management, machine learning and material informatics for Superalloys', online, Bochum, Germany (2021)
Gault, B.: Introduction to atom probe tomography: performance and opportunities in characterizing microstructures. Metallic Microstructures: European Lectures Online (2021)
Antonov, S.; Shi, R.; Li, D.; Kloenne, Z.; Zheng, Y.; Fraser, H. L.; Raabe, D.; Gault, B.: Atom Probe Tomographic Study of Precursor Metastable Phases and Their Influence on a Precipitation in the Metastable ß-titanium Alloy, Ti–5Al–5Mo–5V–3Cr. TMS 2021 Annual Meeting & Exhibition, online, Pittsburgh, PA, USA (2021)
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.