Machine Learning Interatomic Potentials (MLIPs) present a significant advancement in fitting molecular potential energy surfaces and predicting molecular crystal structures and mechanical properties by closely approximating first-principles (FP) calculations results while substantially reducing computational time. In this work, utilizing datasets derived from FP calculations, neural equivariant interatomic potentials (NequIP) and Deep Potential Molecular Dynamics (DPMD) was implemented to develop MLIPs for tobermorite 9, 11, 14 Å. The accuracy and efficiency of NequIP and DPMD were assessed by predicting energies and forces as well as performing molecular dynamics (MD) simulations. The findings indicate that NequIP closely fits the results of FP calculations, with both accuracy and efficiency improved by 1∼2 orders of magnitude compared to the DPMD. This demonstrates that MLIPs developed through NequIP for tobermorites have the capacity to enhance the capability of large-scale molecular dynamics simulations.