Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

Abstract Lithium‐rich Nickel–Cobalt–Manganese (Li‐rich NCM) materials are promising cathodes for lithium‐ion batteries, where electrochemical performance is sensitive to primary particle size. Yet, efforts to apply machine learning (ML) for particle size prediction are constrained by incomplete lite...

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Egile Nagusiak:	Benediktus Madika, Chaeyul Kang, JooSung Shim, Taemin Park, Jung Hyeon Moon, EunAe Cho, Seungbum Hong
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	Wiley 2026-01-01
Saila:	Advanced Science
Gaiak:	chemistry‐aware imputation lithium‐rich natural gradient boosting primary particle size probabilistic machine learning sintering conditions
Sarrera elektronikoa:	https://doi.org/10.1002/advs.202515694
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

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