Home > Publications database > Learning the laws of lithium-ion transport inelectrolytes using symbolic regression† > print

001     1019519
005     20240712113122.0
024 7 _ |a 10.1039/D2DD00027J
|2 doi
024 7 _ |a 10.34734/FZJ-2023-05466
|2 datacite_doi
024 7 _ |a WOS:001101457500001
|2 WOS
037 _ _ |a FZJ-2023-05466
082 _ _ |a 004
100 1 _ |a Flores, Eibar
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a Learning the laws of lithium-ion transport inelectrolytes using symbolic regression†
260 _ _ |a Washington DC
|c 2022
|b Royal Society of Chemistry
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1711453052_4228
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a High-throughput experiments (HTE) enable fast exploration of advanced battery electrolytes over vast compositional spaces. Among the multiple properties considered for optimal electrolyte performance, the conductivity is critical. An analytical expression for ionic transport in electrolytes, accurate for practical compositions and operating conditions, would accelerate the process of (i) co-optimizing conductivity alongside other desirable electrolyte properties, and (ii) learning fundamental physical laws from data, which is one of the paramount goals of scientific big-data analytics. Here, we used symbolic regression with an HTE-acquired dataset of electrolyte conductivity and discovered a simple, accurate, consistent and generalizable expression. Notably, despite emerging from a purely statistical approach, the expression reflects functional aspects from established thermodynamic limiting laws, indicating our model is grounded on the fundamental physical mechanisms underpinning ionic transport. We demonstrate the potential of using machine learning with HTE to find accurate and physically-sound models in complex systems without established physico-chemical theories.
536 _ _ |a 1222 - Components and Cells (POF4-122)
|0 G:(DE-HGF)POF4-1222
|c POF4-122
|f POF IV
|x 0
536 _ _ |a BIG-MAP - Battery Interface Genome - Materials Acceleration Platform (957189)
|0 G:(EU-Grant)957189
|c 957189
|f H2020-LC-BAT-2020-3
|x 1
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Wölke, Christian
|0 P:(DE-Juel1)176954
|b 1
|u fzj
700 1 _ |a Yan, Peng
|0 P:(DE-Juel1)186842
|b 2
|u fzj
700 1 _ |a Winter, Martin
|0 P:(DE-Juel1)166130
|b 3
|u fzj
700 1 _ |a Vegge, Tejs
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Cekic-Laskovic, Isidora
|0 P:(DE-Juel1)171204
|b 5
|u fzj
700 1 _ |a Bhowmik, Arghya
|0 P:(DE-HGF)0
|b 6
|e Corresponding author
773 _ _ |a 10.1039/D2DD00027J
|g Vol. 1, no. 4, p. 440 - 447
|0 PERI:(DE-600)3142965-8
|n 4
|p 440-447
|t Digital discovery
|v 1
|y 2022
|x 2635-098X
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.pdf
856 4 _ |y OpenAccess
|x icon
|u https://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.gif?subformat=icon
856 4 _ |y OpenAccess
|x icon-1440
|u https://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.jpg?subformat=icon-1440
856 4 _ |y OpenAccess
|x icon-180
|u https://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.jpg?subformat=icon-180
856 4 _ |y OpenAccess
|x icon-640
|u https://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.jpg?subformat=icon-640
909 C O |o oai:juser.fz-juelich.de:1019519
|p openaire
|p open_access
|p driver
|p VDB
|p ec_fundedresources
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)176954
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)186842
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)166130
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)171204
913 1 _ |a DE-HGF
|b Forschungsbereich Energie
|l Materialien und Technologien für die Energiewende (MTET)
|1 G:(DE-HGF)POF4-120
|0 G:(DE-HGF)POF4-122
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-100
|4 G:(DE-HGF)POF
|v Elektrochemische Energiespeicherung
|9 G:(DE-HGF)POF4-1222
|x 0
915 _ _ |a Creative Commons Attribution CC BY 3.0
|0 LIC:(DE-HGF)CCBY3
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2022-06-22T13:37:40Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2022-06-22T13:37:40Z
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2022-06-22T13:37:40Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-08-30
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IEK-12-20141217
|k IEK-12
|l Helmholtz-Institut Münster Ionenleiter für Energiespeicher
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IEK-12-20141217
981 _ _ |a I:(DE-Juel1)IMD-4-20141217

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21