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 literature data. This study applies imputation methods-MatImpute, K-nearest neighbors, multivariate imputation by chained equations, and Mean-to complete the datasets, followed by training a Natural Gradient Boosting (NGBoost) model to predict primary particle size with quantified uncertainty. Two training strategies are evaluated: one including entries with imputed target values and another excluding them. Both strategies are tested on a fully observed dataset. The MatImpute-based NGBoost model achieves the

SUBMITTER: Madika B 

PROVIDER: S-EPMC12786289 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature