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dc.contributor.authorBhowmik, A
dc.contributor.authorBerecibar, M
dc.contributor.authorCasas-Cabanas, M
dc.contributor.authorCsanyi, Gabor
dc.contributor.authorDominko, R
dc.contributor.authorHermansson, K
dc.contributor.authorPalacin, MR
dc.contributor.authorStein, HS
dc.contributor.authorVegge, T
dc.date.accessioned2021-11-24T07:17:00Z
dc.date.available2021-11-24T07:17:00Z
dc.date.issued2022-05
dc.date.submitted2021-08-31
dc.identifier.issn1614-6832
dc.identifier.otheraenm202102698
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331016
dc.descriptionFunder: Swedish national Strategic e‐Science programme
dc.descriptionFunder: Deutsche Forschungsgemeinschaft; Id: http://dx.doi.org/10.13039/501100001659
dc.description.abstractBATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries. Here, a description is given of how the AI-assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low-technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI-assisted toolkit developed within BIG-MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time- and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling and explainable AI, as well as AI-assisted materials characterization, self-driving labs, closed-loop optimization, and AI for advanced sensing and self-healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low-TRL battery chemistries and concepts covering multivalent anodes, metal-sulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries.
dc.languageen
dc.publisherWiley
dc.subjectReview
dc.subjectReviews
dc.subjectautonomous discovery
dc.subjectbatteries
dc.subjectexplainable AI
dc.subjectinterface dynamics
dc.subjectmulti‐sourced multi‐scaling
dc.titleImplications of the BATTERY 2030+ AI-Assisted Toolkit on Future Low-TRL Battery Discoveries and Chemistries
dc.typeArticle
dc.date.updated2021-11-24T07:16:59Z
prism.publicationNameAdvanced Energy Materials
dc.identifier.doi10.17863/CAM.78461
dcterms.dateAccepted2021-10-29
rioxxterms.versionofrecord10.1002/aenm.202102698
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.contributor.orcidCsanyi, Gabor [0000-0002-8180-2034]
dc.contributor.orcidVegge, T [0000-0002-1484-0284]
dc.identifier.eissn1614-6840
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Research Infrastructures (RI) (957189)
cam.issuedOnline2021-11-23


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