  Graph neural networks are attractive for learning properties of atomic
structures thanks to their intuitive graph encoding of atoms and bonds.
However, conventional encoding does not include angular information, which is
critical for describing atomic arrangements in disordered systems. In this
work, we extend the recently proposed ALIGNN encoding, which incorporates bond
angles, to also include dihedral angles (ALIGNN-d). This simple extension leads
to a memory-efficient graph representation that captures the complete geometry
of atomic structures. ALIGNN-d is applied to predict the infrared optical
response of dynamically disordered Cu(II) aqua complexes, leveraging the
intrinsic interpretability to elucidate the relative contributions of
individual structural components. Bond and dihedral angles are found to be
critical contributors to the fine structure of the absorption response, with
distortions representing transitions between more common geometries exhibiting
the strongest absorption intensity. Future directions for further development
of ALIGNN-d are discussed.
