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Graph neural networks are attractive for learning properties of atomicstructures thanks to their intuitive graph encoding of atoms and bonds.However, conventional encoding does not include angular information, which iscritical for describing atomic arrangements in disordered systems. In thiswork, we extend the recently proposed ALIGNN encoding, which incorporates bondangles, to also include dihedral angles (ALIGNN-d). This simple extension leadsto a memory-efficient graph representation that captures the complete geometryof atomic structures. ALIGNN-d is applied to predict the infrared opticalresponse of dynamically disordered Cu(II) aqua complexes, leveraging theintrinsic interpretability to elucidate the relative contributions ofindividual structural components. Bond and dihedral angles are found to becritical contributors to the fine structure of the absorption response, withdistortions representing transitions between more common geometries exhibitingthe strongest absorption intensity. Future directions for further developmentof ALIGNN-d are discussed.

Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy