{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["54(Pt 5)"],"submitter":["Kuhbach M"],"funding":["Alexander von Humboldt-Stiftung","Max-Planck-Gesellschaft"],"pubmed_abstract":["Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemical composition features 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 characterization 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 data sets contain substantial noise. "],"journal":["Journal of applied crystallography"],"pagination":["1490-1508"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8493626"],"repository":["biostudies-literature"],"pubmed_title":["Open and strong-scaling tools for atom-probe crystallography: high-throughput methods for indexing crystal structure and orientation."],"pmcid":["PMC8493626"],"pubmed_authors":["Kuhbach M","Gault B","Kasemer M","Breen A"],"additional_accession":[]},"is_claimable":false,"name":"Open and strong-scaling tools for atom-probe crystallography: high-throughput methods for indexing crystal structure and orientation.","description":"Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemical composition features 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 characterization 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 data sets contain substantial noise. ","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2026-06-15T04:51:56.892Z","creation":"2025-04-04T14:40:04.921Z"},"accession":"S-EPMC8493626","cross_references":{"pubmed":["34667452"],"doi":["10.1107/S1600576721008578"]}}