In order to establish the relationship between molecular geometry and RESP charges, we devise the atom representation by utilizing message passing and E3NN. Then, a linear regression model is employed to map the representation to the RESP charges. By incorporating both scalar and tensor information during message passing, E3NN has shown much higher accuracy on potential energy surface fitting task when compared with previous NN models, and is expected to show high accuracy in predicting RESP charges. Consistent with the architecture established in our former work, the molecule representation is updated as