  Dielectrics are materials with widespread applications in flash memory,
central processing units, photovoltaics, capacitors, etc. However, the
availability of public dielectric data remains limited, hindering research and
development efforts. Previously, machine learning models focused on predicting
dielectric constants as scalars, overlooking the importance of dielectric
tensors in understanding material properties under directional electric fields
for material design and simulation. This study demonstrates the value of common
equivariant structural embedding features derived from a universal neural
network potential in enhancing the prediction of dielectric properties. To
integrate channel information from various-rank latent features while
preserving the desired SE(3) equivariance to the second-rank dielectric
tensors, we design an equivariant readout decoder to predict the total,
electronic, and ionic dielectric tensors individually, and compare our model
with the state-of-the-art models. Finally, we evaluate our model by conducting
virtual screening on thermodynamical stable structure candidates in Materials
Project. The material Ba\textsubscript{2}SmTaO\textsubscript{6} with large band
gaps ($E_g=3.36 \mathrm{eV}$) and dielectric constants ($\epsilon=93.81$) is
successfully identified out of the 14k candidate set. The results show that our
methods give good accuracy on predicting dielectric tensors of inorganic
materials, emphasizing their potential in contributing to the discovery of
novel dielectrics.
