h1

h2

h3

h4

h5
h6

001     995227
005     20250220103530.0
024 7 _ |2 ISSN
|a 2041-6520
024 7 _ |2 ISSN
|a 2041-6539
024 7 _ |2 SCOPUS
|a SCOPUS:2-s2.0-85207348287
024 7 _ |2 WOS
|a WOS:001333143100001
024 7 _ |2 doi
|a 10.1039/D4SC04554H
024 7 _ |2 pmid
|a pmid:39430937
024 7 _ |2 datacite_doi
|a 10.18154/RWTH-2024-09801
037 _ _ |a RWTH-2024-09801
041 _ _ |a English
082 _ _ |a 540
100 1 _ |0 P:(DE-82)IDM04034
|a Rittig, Jan Gerald
|b 0
|u rwth
245 _ _ |a Thermodynamics-consistent graph neural networks
|h online, print
260 _ _ |a Cambridge
|b RSC
|c 2024
336 7 _ |0 0
|2 EndNote
|a Journal Article
336 7 _ |0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
|a Journal Article
|b journal
|m journal
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 DRIVER
|a article
336 7 _ |2 DataCite
|a Output Types/Journal article
336 7 _ |2 ORCID
|a JOURNAL_ARTICLE
500 _ _ |a First published 17 Oct 2024
536 _ _ |0 G:(GEPRIS)466417970
|a DFG project G:(GEPRIS)466417970 - Graph-Basiertes Generatives Maschinelles Learnen für optimale Moleküle (466417970)
|c 466417970
|x 0
536 _ _ |0 G:(GEPRIS)441958259
|a DFG project G:(GEPRIS)441958259 - SPP 2331: Maschinelles Lernen in der Verfahrenstechnik. Wissen trifft auf Daten: Interpretierbarkeit, Extrapolation, Verlässlichkeit, Vertrauen (441958259)
|c 441958259
|x 1
588 _ _ |a Dataset connected to CrossRef, Journals: publications.rwth-aachen.de
591 _ _ |a Germany
700 1 _ |0 P:(DE-82)IDM00369
|a Mitsos, Alexander
|b 1
|e Corresponding author
|u rwth
773 _ _ |0 PERI:(DE-600)2559110-1
|a 10.1039/D4SC04554H
|n 44
|p 18504-18512
|t Chemical science
|v 15
|x 2041-6539
|y 2024
856 4 _ |u https://publications.rwth-aachen.de/record/995227/files/995227.pdf
|y OpenAccess
909 C O |o oai:publications.rwth-aachen.de:995227
|p openaire
|p open_access
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |0 I:(DE-588b)36225-6
|6 P:(DE-82)IDM04034
|a RWTH Aachen
|b 0
|k RWTH
910 1 _ |0 I:(DE-588b)36225-6
|6 P:(DE-82)IDM00369
|a RWTH Aachen
|b 1
|k RWTH
914 1 _ |y 2024
915 1 _ |0 StatID:(DE-HGF)0031
|2 StatID
|a Peer reviewed article
|x 0
915 _ _ |0 LIC:(DE-HGF)CCBY3
|2 HGFVOC
|a Creative Commons Attribution CC BY 3.0
915 _ _ |0 StatID:(DE-HGF)0150
|2 StatID
|a DBCoverage
|b Web of Science Core Collection
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)0300
|2 StatID
|a DBCoverage
|b Medline
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)0100
|2 StatID
|a JCR
|b CHEM SCI : 2022
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)9905
|2 StatID
|a IF >= 5
|b CHEM SCI : 2022
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)0501
|2 StatID
|a DBCoverage
|b DOAJ Seal
|d 2020-12-17T14:34:32Z
915 _ _ |0 StatID:(DE-HGF)0113
|2 StatID
|a WoS
|b Science Citation Index Expanded
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)1210
|2 StatID
|a DBCoverage
|b Index Chemicus
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)0500
|2 StatID
|a DBCoverage
|b DOAJ
|d 2020-12-17T14:34:32Z
915 _ _ |0 StatID:(DE-HGF)0510
|2 StatID
|a OpenAccess
915 _ _ |0 StatID:(DE-HGF)0030
|2 StatID
|a Peer Review
|b DOAJ : Anonymous peer review
|d 2020-12-17T14:34:32Z
915 _ _ |0 StatID:(DE-HGF)1150
|2 StatID
|a DBCoverage
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)1200
|2 StatID
|a DBCoverage
|b Chemical Reactions
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)0430
|2 StatID
|a National-Konsortium
|d 2023-10-26
|w ger
915 _ _ |0 StatID:(DE-HGF)0160
|2 StatID
|a DBCoverage
|b Essential Science Indicators
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)0200
|2 StatID
|a DBCoverage
|b SCOPUS
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)0320
|2 StatID
|a DBCoverage
|b PubMed Central
|d 2023-10-26
915 _ _ |0 StatID:(DE-HGF)0199
|2 StatID
|a DBCoverage
|b Clarivate Analytics Master Journal List
|d 2023-10-26
920 1 _ |0 I:(DE-82)416710_20140620
|k 416710
|l Lehrstuhl für Systemverfahrenstechnik
|x 0
920 1 _ |0 I:(DE-82)080011_20140620
|k 080011
|l JARA-ENERGY
|x 1
980 1 _ |a FullTexts
980 _ _ |a I:(DE-82)080011_20140620
980 _ _ |a I:(DE-82)416710_20140620
980 _ _ |a UNRESTRICTED
980 _ _ |a VDB
980 _ _ |a journal
980 _ _ |a APC

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21