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Density functional theory and machine learning for electrochemical square-scheme prediction: an application to quinone-type molecules relevant to redox flow batteries
Hashemi, Arsalan
Department of Chemistry and Material Science, School of Chemical Engineering, Aalto University, 02150 Espoo, Finland.
ORCID iD:
0000-0003-2699-5731
Khakpour, Reza
Department of Chemistry and Material Science, School of Chemical Engineering, Aalto University, 02150 Espoo, Finland.
Mahdian, Amir
Department of Chemistry and Material Science, School of Chemical Engineering, Aalto University, 02150 Espoo, Finland.
Busch, Michael
Institute of Theoretical Chemistry, Ulm University, Albert-Einstein Allee 11, 89069 Ulm, Germany.
ORCID iD:
0000-0003-3883-2868
Peljo, Pekka
Research Group of Battery Materials and Technologies, Department of Mechanical and Materials Engineering, Faculty of Technology, University of Turku, 20014 Turun Yliopisto, Finland.
ORCID iD:
0000-0002-1229-2261
Laasonen, Kari
Department of Chemistry and Material Science, School of Chemical Engineering, Aalto University, 02150 Espoo, Finland.
ORCID iD:
0000-0002-4419-7824
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2023 (English)
In:
Digital Discovery, E-ISSN 2635-098X, Vol. 2, no 5, p. 1565-1576
Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2023. Vol. 2, no 5, p. 1565-1576
National Category
Chemical Sciences
Identifiers
URN:
urn:nbn:se:ltu:diva-104054
DOI:
10.1039/d3dd00091e
ISI:
001105264600001
Scopus ID:
2-s2.0-85172795641
OAI: oai:DiVA.org:ltu-104054
DiVA, id:
diva2:1833640
Funder
Academy of Finland, 315739
EU, Horizon 2020, 875565
Available from:
2024-02-01
Created:
2024-02-01
Last updated:
2024-03-09
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Busch, Michael
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Hashemi, Arsalan
Busch, Michael
Peljo, Pekka
Laasonen, Kari
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