Torkornoo, S.; Bohner, M.; McCarroll, I.; Gault, B.: Optimization of Parameters for Atom Probe Tomography Analysis of β-Tricalcium Phosphates. Microscopy and Microanalysis 30 (6), pp. 1074 - 1082 (2024)
Li, Y.; Gault, B.: Machine Learning Enhanced Tomographic Imaging of Chemical Short-range Order in Fe-based Solid Solutions. Microscopy and Microanalysis 30 (Supplement_1), pp. 44 - 45 (2024)
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
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.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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…