Al‐Doping Driven Suppression of Capacity and Voltage Fadings in 4d‐Element Containing Li‐Ion‐Battery Cathode Materials: Machine Learning and Density Functional Theory

Abstract The anion redox reaction in high‐energy‐density cathode materials such as Li‐excess layered oxides suffers from voltage/capacity fadings due to irreversible structural instability. Here, exploiting density functional theory (DFT) as well as fast simulations using the universal potential/forces generated from the newly developed sparse Gaussian process regression (SGPR) machine learning (ML) method, the very complicated/complex structures, X‐ray absorption near‐edge‐structure (XANES) spectra, redox phenomena, and Li diffusion of these battery materials depending on charging/discharging processes is investigated. It is found that voltage/capacity fadings are strongly suppressed in 4d‐element‐containing cathodes by Al‐doping. The suppressed fadings are discussed in view of the structural and electronic changes depending on charged/discharged states which are reflected in their extended X‐ray absorption fine structure and XANES spectra. According to crystal orbital Hamilton populations (COHP) and Bader charge analyses of Li 1.22 Ru 0.61 Ni 0.11 Al 0.06 O 2 (Al‐LRNO), the Al‐doping helps in forming Ni–Al bonding and hence strengthens the bonding‐orbital characteristics in Al–O bonds. This strengthened Al–O bonding hinders oxygen oxidation and thus enhances structural stability, diminishing safety concerns. The Al‐doping driven suppression of capacity fading and voltage decay is expected to help in designing stable reversible layered cathode materials.