Antonov, S.; Li, B.; Gault, B.; Tan, Q.: The effect of solute segregation to deformation twin boundaries on the electrical resistivity of a single-phase superalloy. Scripta Materialia 186, pp. 208 - 212 (2020)
Blum, T.; Valley, J.; Gault, B.; Stephenson, L.: Application of SIMS and APT to Understand Scale Dependent U-Pb Isotope Behavior in Zircon. Microscopy and Microanalysis 26 (S2), pp. 2994 - 2995 (2020)
Harding, I.; Mouton, I.; Gault, B.; Kumar, K. S.: Microstructural Evolution in an Fe–10Ni–0.1C Steel During Heat Treatment and High Strain-Rate Deformation. Metallurgical and Materials Transactions A 51, pp. 5056 - 5076 (2020)
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
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…