Data‐Driven Decoupling Structural Feature Correlation for Harnessing Anionic Capacity in Na Layered Oxide

Abstract

A data‐driven understanding of the reaction mechanism of the O3‐type Na[Li1/3Mn2/3]O2‐layered cathode model is systematically performed to decouple the complex correlations of covariate scale‐dependent variables in diagnosing and solving structural problems for facilitating nonhysteretic and reversible (nHR) oxygen capacities using interpretable machine learning (ML) assisted by density functional theory. A large dataset of vacancy formation energies depending on the desodiation mode for the oxide is investigated in detail, and it provides two numerical principles: i) linearizing the energy landscape and ii) steepening its slope to reach the ideal reaction. The heatmaps comprising Pearson coefficient correlation values are broken down into two‐scale components: i) macroscopic and ii) local structure features. Deriving the overall mitigation of scale‐dependent covariate variables in negative correlation potentially leads to nHR anionic redox upon (dis)charging. Containing the scale‐dependent features, the interpretable ML model based on a gradient‐boosting machine predicts each formation energy well. With data‐driven comprehension, honeycomb‐ and turtle‐type superstructures (TS) have been suggested depending on the thermodynamic (un)favorable pathways during desodiation from a local structural perspective, and the dangling O2– in the TS is a critical origin leading to the formation of O2 molecules trapped in the bulk.