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

Graph neural networks (GNNs) are attractive for learning properties of atomic structures thanks to their intuitive, physically informed graph encoding of atoms and bonds. However, conventional GNN encodings do not account for angular information, which is critical for describing complex atomic arrangements in disordered materials, interfaces, and molecular distortions. In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d), and we apply the model to capture the structures of aqua copper complexes for spectroscopy prediction. This simple extension is shown to lead to a memory-efficient graph representation capable of capturing the full geometric information of atomic structures. Specifically, the ALIGNN-d encoding is a sparse yet equally expressive representation compared to the dense, maximally-connected graph, in which all bonds are encoded. We also explore model interpretability based on ALIGNN-d by elucidating the relative contributions of individual structural components to the optical response of the copper complexes. Lastly, we briefly discuss future developments to validate the computational efficiency and to extend the interpretability of ALIGNN-d.