Lithium-ion diffusion can be accurately predicted through first-principal ab initio calculations. However, the computationally expense associated with these calculations, particularly when the dealing with systems containing a large number of atoms, such as ours, can be prohibitive. Therefore, there is a compelling need for alternative approaches that can achieve comparable accuracy in significantly less time. To address this challenge, we utilized molecular dynamics simulations (MD) employing the dispersion corrected M3GNet neural network potential , using AMS software package . This neural network potential has demonstrated excellent in describing various battery materials and was trained using first-principle ab initio calculations from the Materials project . The validity of M3GNet neural network potential for LMNO simulation was confirmed as shown in Supplementary Materials (Figure ) by comparing with the corresponding first principle calculation using BAND planewave DFT code available in AMS software package .