Rethinking the role of frames for SE(3)-invariant crystal structure modeling
Keywords: Materials Science, Invariant Networks, Transformer, Physics-Informed ML
TL;DR: Propose dynamic atom-wise frames aligned with attention-based interatomic interactions on the structure, rather than with the structure itself.
Abstract: Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamental requirement for these networks. A straightforward approach is to model with orientation-standardized structures through structure-aligned coordinate systems, or “frames.” However, unlike molecules, determining frames for crystal structures is challenging due to their infinite and highly symmetric nature. In particular, existing methods rely on a statically fixed frame for each structure, determined solely by its structural information, regardless of the task under consideration. Here, we rethink the role of frames, *questioning whether such simplistic alignment with the structure is sufficient*, and propose the concept of *dynamic frames*. While accommodating the infinite and symmetric nature of crystals, these frames provide each atom with a dynamic view of its local environment, focusing on actively interacting atoms. We demonstrate this concept by utilizing the attention mechanism in a recent transformer-based crystal encoder, resulting in a new architecture called **CrystalFramer**. Extensive experiments show that CrystalFramer outperforms conventional frames and existing crystal encoders in various crystal property prediction tasks.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 5622
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