Exploring Electrolyte\nAdsorption on the Different\nTypes of Layered Cathode Surfaces in Lithium-Ion Batteries via a Universal\nNeural Network Potential Method

LiNi<sub><i>x</i></sub>Mn<sub><i>y</i></sub>Co<sub><i>z</i></sub>O<sub>2</sub> (NCM) is a promising\ncathode material for lithium-ion batteries. Their long-term stability\nand impedance growth as cycles are strongly influenced by the properties\nof the cathode electrolyte interface (CEI), formed by the reaction\nbetween the electrolyte and electrode surface. Understanding of these\ninteractions at the atomic level of the NCM electrode surface and\nelectrolyte will provide a new strategic approach for the design of\na highly functional CEI layer. In this study, we explored the influence\nof Ni content in transition metal layers on surface energies under\ndifferent synthetic conditions and terminations using a density functional\ntheory (DFT) validated universal neural network potential (UNNP) method.\nFurthermore, we investigated the adsorption of ethylene carbonate\n(EC) and dimethyl carbonate (DMC) on the most favorable NCM surfaces.\nEC and DMC displayed similar adsorption energy trends; however, differences\nwere observed in the preferred configurations, which can affect the\nformation of the CEI.