  The dipole moment is a physical quantity indicating the polarity of a
molecule and is determined by reflecting the electrical properties of
constituent atoms and the geometric properties of the molecule. Most embeddings
used to represent graph representations in traditional graph neural network
methodologies treat molecules as topological graphs, creating a significant
barrier to the goal of recognizing geometric information. Unlike existing
embeddings dealing with equivariance, which have been proposed to handle the 3D
structure of molecules properly, our proposed embeddings directly express the
physical implications of the local contribution of dipole moments. We show that
the developed model works reasonably even for molecules with extended
geometries and captures more interatomic interaction information, significantly
improving the prediction results with accuracy comparable to ab-initio
calculations.
