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 Li1.22Ru0.61Ni0.11Al0.06O2 (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.