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The dipole moment is a physical quantity indicating the polarity of amolecule and is determined by reflecting the electrical properties ofconstituent atoms and the geometric properties of the molecule. Most embeddingsused to represent graph representations in traditional graph neural networkmethodologies treat molecules as topological graphs, creating a significantbarrier to the goal of recognizing geometric information. Unlike existingembeddings dealing with equivariance, which have been proposed to handle the 3Dstructure of molecules properly, our proposed embeddings directly express thephysical implications of the local contribution of dipole moments. We show thatthe developed model works reasonably even for molecules with extendedgeometries and captures more interatomic interaction information, significantlyimproving the prediction results with accuracy comparable to ab-initiocalculations.

Edge Direction-invariant Graph Neural Networks for Molecular Dipole Moments Prediction