Machine learning assisted synthesis of lithium-ion batteries cathode materials

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dc.contributor.authorLiow, Chi Haoko
dc.contributor.authorKang, Hyeonmukko
dc.contributor.authorKim, Seungguko
dc.contributor.authorNa, Moonyko
dc.contributor.authorLee, Yongjuko
dc.contributor.authorBaucour, Arthurko
dc.contributor.authorBang, Kihoonko
dc.contributor.authorShim, Yoonsuko
dc.contributor.authorChoe, Jacobko
dc.contributor.authorHwang, Gyuseongko
dc.contributor.authorCho, Seongwooko
dc.contributor.authorPark, Gunko
dc.contributor.authorYeom, Jiwonko
dc.contributor.authorAgar, Joshua C.ko
dc.contributor.authorYuk, Jong Minko
dc.contributor.authorShin, Jonghwako
dc.contributor.authorLee, Hyuck Moko
dc.contributor.authorByon, Hye Ryungko
dc.contributor.authorCho, EunAeko
dc.contributor.authorHong, Seungbumko
dc.identifier.citationNANO ENERGY, v.98-
dc.description.abstractOptimizing synthesis parameters is crucial in fabricating an ideal cathode material; however, the design space is too vast to be fully explored using an Edisonian approach. Here, by clustering eleven domain-expert-deriveddescriptors from literature, we use an inverse design surrogate model to build up the experimental parameters-property relationship. Without struggling with the trial-and-error method, the model enables design variables prediction that serves as an effective strategy for cathode retrosynthesis. More importantly, not only did we overcome the data scarcity problem, but the machine learning model has guided us to achieve cathode with high discharge capacity and Coulombic efficiency of 209.5 mAh/g and 86%, respectively. This work demonstrates an inverse design-to-device pipeline with unprecedented potential to accelerate the discovery of highenergy-density cathodes.-
dc.titleMachine learning assisted synthesis of lithium-ion batteries cathode materials-
dc.citation.publicationnameNANO ENERGY-
dc.contributor.localauthorYuk, Jong Min-
dc.contributor.localauthorShin, Jonghwa-
dc.contributor.localauthorLee, Hyuck Mo-
dc.contributor.localauthorByon, Hye Ryung-
dc.contributor.localauthorCho, EunAe-
dc.contributor.localauthorHong, Seungbum-
dc.contributor.nonIdAuthorLiow, Chi Hao-
dc.contributor.nonIdAuthorChoe, Jacob-
dc.contributor.nonIdAuthorHwang, Gyuseong-
dc.contributor.nonIdAuthorAgar, Joshua C.-
dc.subject.keywordAuthorLithium-ion batteries-
dc.subject.keywordAuthorNCM cathode-
dc.subject.keywordAuthorInverse design-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorDesign-to-device pipeline-
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MS-Journal Papers(저널논문)CH-Journal Papers(저널논문)
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