Li, Y.; Gault, B.: Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography. BiGmax Spring Meeting 2022, Bochum, Germany (2022)
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)
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.