The goal of this work is to investigate the capability of a neural operator (DeepONet) to accurately capture the complex deformation of a platelet membrane under shear flow. The surrogate model approximated by the neural operator predicts the configuration of the deformed membrane based on its initial configuration and the shear stress exerted by the blood flow. The training dataset is derived from particle dynamics simulations implemented in LAMMPS. The neural operator captures the dynamics of the membrane particles with a mode error distribution of approximately 0.5%. The proposed implementation serves as a scalable approach to integrate sub-platelet dynamics into multi-scale computational models of thrombosis.