date: 2022-01-11T07:02:38Z pdf:PDFVersion: 1.7 pdf:docinfo:title: Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data xmp:CreatorTool: Elsevier AuthoritativeDomain[2]: elsevier.com access_permission:can_print_degraded: true subject: Acta Materialia, 226 (2022) 117633. doi:10.1016/j.actamat.2022.117633 dc:format: application/pdf; version=1.7 pdf:docinfo:custom:robots: noindex pdf:docinfo:creator_tool: Elsevier access_permission:fill_in_form: true pdf:encrypted: false dc:title: Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data modified: 2022-01-11T07:02:38Z cp:subject: Acta Materialia, 226 (2022) 117633. doi:10.1016/j.actamat.2022.117633 pdf:docinfo:custom:CrossMarkDomains[1]: sciencedirect.com robots: noindex pdf:docinfo:subject: Acta Materialia, 226 (2022) 117633. doi:10.1016/j.actamat.2022.117633 pdf:docinfo:creator: Xuyang Zhou meta:author: Ye Wei meta:creation-date: 2022-01-11T07:01:37Z pdf:docinfo:custom:CrossmarkMajorVersionDate: 2010-04-23 created: 2022-01-11T07:01:37Z access_permission:extract_for_accessibility: true Creation-Date: 2022-01-11T07:01:37Z pdf:docinfo:custom:CrossMarkDomains[2]: elsevier.com ElsevierWebPDFSpecifications: 7.0 pdf:docinfo:custom:doi: 10.1016/j.actamat.2022.117633 pdf:docinfo:custom:CrossmarkDomainExclusive: true Author: Ye Wei producer: Acrobat Distiller 10.0.0 (Windows) CrossmarkDomainExclusive: true pdf:docinfo:producer: Acrobat Distiller 10.0.0 (Windows) doi: 10.1016/j.actamat.2022.117633 pdf:unmappedUnicodeCharsPerPage: 0 dc:description: Acta Materialia, 226 (2022) 117633. doi:10.1016/j.actamat.2022.117633 access_permission:modify_annotations: true pdf:docinfo:custom:AuthoritativeDomain[2]: elsevier.com dc:creator: Ye Wei description: Acta Materialia, 226 (2022) 117633. doi:10.1016/j.actamat.2022.117633 dcterms:created: 2022-01-11T07:01:37Z Last-Modified: 2022-01-11T07:02:38Z dcterms:modified: 2022-01-11T07:02:38Z title: Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data xmpMM:DocumentID: uuid:fd8cfc10-4791-4a8b-8025-00abb91efa0c Last-Save-Date: 2022-01-11T07:02:38Z CrossMarkDomains[1]: sciencedirect.com pdf:docinfo:modified: 2022-01-11T07:02:38Z meta:save-date: 2022-01-11T07:02:38Z Content-Type: application/pdf X-Parsed-By: org.apache.tika.parser.DefaultParser creator: Ye Wei AuthoritativeDomain[1]: sciencedirect.com pdf:docinfo:custom:AuthoritativeDomain[1]: sciencedirect.com pdf:docinfo:custom:ElsevierWebPDFSpecifications: 7.0 access_permission:assemble_document: true xmpTPg:NPages: 15 pdf:charsPerPage: 4860 access_permission:extract_content: true access_permission:can_print: true CrossMarkDomains[2]: elsevier.com access_permission:can_modify: true pdf:docinfo:created: 2022-01-11T07:01:37Z CrossmarkMajorVersionDate: 2010-04-23