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Gibbs–Duhem-informed neural networks for binary activity coefficient prediction

; ; ;

In
Digital discovery 2(6), Seiten/Artikel-Nr.:1752-1767

ImpressumCambridge ; London ; Washington DC : Royal Society of Chemistry

ISSN2635-098X

Online
DOI: 10.1039/D3DD00103B

DOI: 10.18154/RWTH-2023-09881
URL: https://publications.rwth-aachen.de/record/971901/files/971901.pdf

Einrichtungen

  1. Lehrstuhl für Systemverfahrenstechnik (416710)
  2. JARA-ENERGY (080011)


Thematische Einordnung (Klassifikation)
DDC: 004

OpenAccess:
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Dokumenttyp
Journal Article

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85174171974
WOS Core Collection: WOS:001112646600001

Interne Identnummern
RWTH-2023-09881
Datensatz-ID: 971901

Beteiligte Länder
Germany, UK

Lizenzstatus der Zeitschrift

 GO

Medline ; Creative Commons Attribution CC BY 3.0 ; DOAJ ; OpenAccess ; DOAJ Seal
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Faculty of Mechanical Engineering (Fac.4)
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080011
416710
TypAmountVATCurrencyShareStatusCost centre
Hybrid-OA0.000.00EUR (Zahlung erfolgt) 
Sum0.000.00EUR   
Total0.00     
 Record created 2023-10-18, last modified 2026-01-10
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