Abstract
Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemistry and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterisation tasks. For atom probe tomography (APT) experiments, however, the strategy of making comparisons between measured and analytically computed patterns is less robust because many APT datasets may contain substantial noise. Given that general enough predictive models for such noise remain elusive, crystallography tools for APT face several limitations: Their robustness to noise, and therefore, their capability to identify and distinguish different crystal structures and orientation is limited. In addition, the tools are sequential and demand substantial manual interaction. In combination, this makes robust uncertainty quantifying with automated high-throughput studies of the latent crystallographic information a difficult task with APT data. To improve the situation, we review the existent methods and discuss how they link to those in the diffraction communities. With this we modify some of the APT methods to yield more robust descriptors of the atomic arrangement. We report how this enables the development of an open-source software tool for strong-scaling and automated identifying of crystal structure and mapping crystal orientation in nanocrystalline APT datasets with multiple phases.
Abstract (translated)
URL
https://arxiv.org/abs/2009.00735
