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

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 highest accuracy, with a test R² of 0.866 and a calibration error of 0.133. Feature analysis identifies second sintering temperature and first sintering time as dominant factors, with composition showing minimal influence, consistent with prior experimental reports that sintering parameters drive sub-micron grain growth through atomic mobility and grain coarsening. Experimental validation shows most predictions within 0.13 µm of measurements and normalized uncertainties near 1.5. These findings demonstrate that robust imputation and uncertainty quantification enhance ML-based particle size prediction and confirm that sintering conditions, rather than stoichiometry, govern microstructural evolution in Li-rich NCM materials.