date: 2022-12-14T07:05:48Z pdf:PDFVersion: 1.7 pdf:docinfo:title: Unified representation of molecules and crystals for machine learning xmp:CreatorTool: XeLateX with hyperref package access_permission:can_print_degraded: true subject: Machine Learning: Science and Technology, 3 (2022) 045017 doi: 10.1088/2632-2153/aca005 language: dc:format: application/pdf; version=1.7 pdf:docinfo:creator_tool: XeLateX with hyperref package access_permission:fill_in_form: true pdf:encrypted: false dc:title: Unified representation of molecules and crystals for machine learning modified: 2022-12-14T07:05:48Z cp:subject: Machine Learning: Science and Technology, 3 (2022) 045017 doi: 10.1088/2632-2153/aca005 pdf:docinfo:subject: Machine Learning: Science and Technology, 3 (2022) 045017 doi: 10.1088/2632-2153/aca005 pdf:docinfo:creator: Haoyan Huo,Matthias Rupp meta:author: Haoyan Huo,Matthias Rupp meta:creation-date: 2022-11-16T21:01:00Z created: 2022-11-16T21:01:00Z access_permission:extract_for_accessibility: true Creation-Date: 2022-11-16T21:01:00Z Author: Haoyan Huo,Matthias Rupp producer: XeLateX; modified using iText® 5.5.13.3 ©2000-2022 iText Group NV (AGPL-version) pdf:docinfo:producer: XeLateX; modified using iText® 5.5.13.3 ©2000-2022 iText Group NV (AGPL-version) pdf:unmappedUnicodeCharsPerPage: 0 dc:description: Machine Learning: Science and Technology, 3 (2022) 045017 doi: 10.1088/2632-2153/aca005 Keywords: many-body tensor representation,machine-learning potential,atomistic simulations access_permission:modify_annotations: true dc:creator: Haoyan Huo,Matthias Rupp description: Machine Learning: Science and Technology, 3 (2022) 045017 doi: 10.1088/2632-2153/aca005 dcterms:created: 2022-11-16T21:01:00Z Last-Modified: 2022-12-14T07:05:48Z dcterms:modified: 2022-12-14T07:05:48Z title: Unified representation of molecules and crystals for machine learning xmpMM:DocumentID: uuid:333905f2-0054-4cea-8d7a-3900595f754c Last-Save-Date: 2022-12-14T07:05:48Z pdf:docinfo:keywords: many-body tensor representation,machine-learning potential,atomistic simulations pdf:docinfo:modified: 2022-12-14T07:05:48Z meta:save-date: 2022-12-14T07:05:48Z Content-Type: application/pdf X-Parsed-By: org.apache.tika.parser.DefaultParser creator: Haoyan Huo,Matthias Rupp dc:language: dc:subject: many-body tensor representation,machine-learning potential,atomistic simulations access_permission:assemble_document: true xmpTPg:NPages: 11 pdf:charsPerPage: 786 access_permission:extract_content: true access_permission:can_print: true meta:keyword: many-body tensor representation,machine-learning potential,atomistic simulations access_permission:can_modify: true pdf:docinfo:created: 2022-11-16T21:01:00Z