Chemical mechanical polishing (CMP) is a process of surface planarization by chemical and mechanical driving forces. CMP is expected to solve various problems, including the surface roughness on semiconductor devices. An aqueous slurry, including abrasive grains and chemical components, is used in the CMP process. Development of slurries with high polishing rate, excellent planarity, and superior processability is not only a key challenge, but also a significant opportunity for the future of our industry. In order to achieve the development, it is necessary to understand the detailed mechanism of CMP, which involves complex phenomena accompanied by interfacial chemical reactions and mechanical friction. Computational scientific approaches, such as density functional theory (DFT) and molecular dynamics (MD) simulations, are powerful tools for understanding complex tribochemical phenomena at the atomic scale. However, these methods require much computation time and present difficulties for large-scale simulations. To overcome this limitation, a computational method called Neural Network Potential (NNP) is a powerful tool because it achieves a good balance between accuracy and computational efficiency by constructing potential energy surfaces based on a neural network-based method. We performed NNP-MD simulations of the ceria/silica interface with the development of CMP slurry including ceria abrasive grains, which is typically used on the surfaces of silicon layered devices. As a result, we succeeded in observing polishing phenomena at the interface at the atomic scale, namely Ce-O-Si bond formation between ceria abrasive grains and silica substrate, hydrolysis reaction of Si-O bond, and associated mono-silica dissociation from the substrate. We also performed simulations of systems containing chemical compounds added to the slurry for the purpose of controlling the polishing rate and observed the role of additives on the CMP process. In this presentation, we will show simulation results and discuss the mechanism of CMP of silica substrates with ceria abrasive grains in terms of dependence on surface morphology at the atomic scale and the role of additives. Reference [1] Tran*, R., Lan*, J., Shuaibi*, M., Wood*, B., Goyal*, S., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., Sriram, A., Ulissi, Z., & Zitnick, C. (2022). The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis. arXiv preprint arXiv:2206.08917.