# Geometric fragment-based attention

Polymer property prediction has long relied on molecular fragments cut by chemical rules, which may suit unfamiliar structures poorly and offer limited control over fragment size. A geometry-based fragmentation method casts fragmentation as a radius-constrained center-selection problem, where the number of fragments follows from a given radius rather than being fixed in advance, and changing the radius yields fragments at different scales. Feeding these multiscale fragments into a multimodal model that combines p-SMILES with 3D conformations lowers prediction error by roughly 5–10% relative to the baseline on several properties of a polymer benchmark, though the 3D modality's contribution varies by property.

Polymer property prediction has long relied on molecular fragments cut by chemical rules, which may suit unfamiliar structures poorly and offer limited control over fragment size. A geometry-based fragmentation method casts fragmentation as a radius-constrained center-selection problem, where the number of fragments follows from a given radius rather than being fixed in advance, and changing the radius yields fragments at different scales. Feeding these multiscale fragments into a multimodal model that combines p-SMILES with 3D conformations lowers prediction error by roughly 5–10% relative to the baseline on several properties of a polymer benchmark, though the 3D modality's contribution varies by property.