Computational Screening for Design of Optimal Coating Materials to Suppress Gas Evolution in Li-Ion Battery Cathodes.

Ni-rich layered oxides are attractive materials owing to their potentially high capacity for cathode applications. However, when used as cathodes in Li-ion batteries, they contain a large amount of Li residues, which degrade the electrochemical properties because they are the source of gas generation inside the battery. Here, we propose a computational approach to designing optimal coating materials that prevent gas evolution by removing residual Li from the surface of the battery cathode. To discover promising coating materials, the reactions of 16 metal phosphates (MPs) and 45 metal oxides (MOs) with the Li residues, LiOH, and LiCO are examined within a thermodynamic framework. A materials database is constructed according to density functional theory using a hybrid functional, and the reaction products are obtained according to the phases in thermodynamic equilibrium in the phase diagram. In addition, the gravimetric efficiency is calculated to identify coating materials that can eliminate Li residues with a minimal weight of the coating material. Overall, more MP and MO materials react with LiOH than with LiCO. Specifically, MPs exhibit better reactivity to both Li residues, whereas MOs react more with LiOH. The reaction products, such as Li-containing phosphates or oxides, are also obtained to identify the phases on the surface of a cathode after coating. On the basis of the Pareto-front analysis, PO could be an optimal material for the reaction with both Li residuals. Finally, the reactivity of the coating materials containing 3d/4d transition metal elements is better than that of materials containing other types of elements.