001019519 001__ 1019519
001019519 005__ 20240712113122.0
001019519 0247_ $$2doi$$a10.1039/D2DD00027J
001019519 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-05466
001019519 0247_ $$2WOS$$aWOS:001101457500001
001019519 037__ $$aFZJ-2023-05466
001019519 082__ $$a004
001019519 1001_ $$0P:(DE-HGF)0$$aFlores, Eibar$$b0$$eCorresponding author
001019519 245__ $$aLearning the laws of lithium-ion transport inelectrolytes using symbolic regression†
001019519 260__ $$aWashington DC$$bRoyal Society of Chemistry$$c2022
001019519 3367_ $$2DRIVER$$aarticle
001019519 3367_ $$2DataCite$$aOutput Types/Journal article
001019519 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1711453052_4228
001019519 3367_ $$2BibTeX$$aARTICLE
001019519 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001019519 3367_ $$00$$2EndNote$$aJournal Article
001019519 520__ $$aHigh-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.
001019519 536__ $$0G:(DE-HGF)POF4-1222$$a1222 - Components and Cells (POF4-122)$$cPOF4-122$$fPOF IV$$x0
001019519 536__ $$0G:(EU-Grant)957189$$aBIG-MAP - Battery Interface Genome - Materials Acceleration Platform (957189)$$c957189$$fH2020-LC-BAT-2020-3$$x1
001019519 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001019519 7001_ $$0P:(DE-Juel1)176954$$aWölke, Christian$$b1$$ufzj
001019519 7001_ $$0P:(DE-Juel1)186842$$aYan, Peng$$b2$$ufzj
001019519 7001_ $$0P:(DE-Juel1)166130$$aWinter, Martin$$b3$$ufzj
001019519 7001_ $$0P:(DE-HGF)0$$aVegge, Tejs$$b4
001019519 7001_ $$0P:(DE-Juel1)171204$$aCekic-Laskovic, Isidora$$b5$$ufzj
001019519 7001_ $$0P:(DE-HGF)0$$aBhowmik, Arghya$$b6$$eCorresponding author
001019519 773__ $$0PERI:(DE-600)3142965-8$$a10.1039/D2DD00027J$$gVol. 1, no. 4, p. 440 - 447$$n4$$p440-447$$tDigital discovery$$v1$$x2635-098X$$y2022
001019519 8564_ $$uhttps://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.pdf$$yOpenAccess
001019519 8564_ $$uhttps://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.gif?subformat=icon$$xicon$$yOpenAccess
001019519 8564_ $$uhttps://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001019519 8564_ $$uhttps://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$$xicon-180$$yOpenAccess
001019519 8564_ $$uhttps://juser.fz-juelich.de/record/1019519/files/Learning%20the%20laws%20of%20lithium-ion%20transport%20inelectrolytes%20using%20symbolic%20regression%E2%80%A0.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001019519 909CO $$ooai:juser.fz-juelich.de:1019519$$pdnbdelivery$$pec_fundedresources$$pVDB$$pdriver$$popen_access$$popenaire
001019519 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176954$$aForschungszentrum Jülich$$b1$$kFZJ
001019519 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186842$$aForschungszentrum Jülich$$b2$$kFZJ
001019519 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)166130$$aForschungszentrum Jülich$$b3$$kFZJ
001019519 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171204$$aForschungszentrum Jülich$$b5$$kFZJ
001019519 9131_ $$0G:(DE-HGF)POF4-122$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1222$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vElektrochemische Energiespeicherung$$x0
001019519 915__ $$0LIC:(DE-HGF)CCBY3$$2HGFVOC$$aCreative Commons Attribution CC BY 3.0
001019519 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2022-06-22T13:37:40Z
001019519 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2022-06-22T13:37:40Z
001019519 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001019519 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2022-06-22T13:37:40Z
001019519 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-30
001019519 920__ $$lyes
001019519 9201_ $$0I:(DE-Juel1)IEK-12-20141217$$kIEK-12$$lHelmholtz-Institut Münster Ionenleiter für Energiespeicher$$x0
001019519 9801_ $$aFullTexts
001019519 980__ $$ajournal
001019519 980__ $$aVDB
001019519 980__ $$aUNRESTRICTED
001019519 980__ $$aI:(DE-Juel1)IEK-12-20141217
001019519 981__ $$aI:(DE-Juel1)IMD-4-20141217