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 (Atomistic Line Graph Neural Network) 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 that represent transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.

图神经网络因其对原子和键的直观图形编码而对学习原子结构的特性很有吸引力。然而，传统的编码不包括角度信息，这对于描述无序系统中的原子排列至关重要。在这项工作中，我们扩展了最近提出的 ALIGNN（原子线图神经网络）编码，它结合了键角，也包括二面角 (ALIGNN-d)。这个简单的扩展导致了一个内存高效的图形表示，它捕获了原子结构的完整几何形状。ALIGNN-d 用于预测动态无序 Cu(II) 水配合物的红外光学响应，利用内在的可解释性来阐明各个结构组分的相对贡献。发现键角和二面角是吸收响应精细结构的关键因素，其中的扭曲代表了表现出最强吸收强度的更常见几何形状之间的过渡。讨论了 ALIGNN-d 进一步发展的未来方向。