PolymerArena: Benchmarking Multi-Task Graph Learning Models on Polymer Property Prediction

Graph neural networks (GNNs) are widely used for molecular property prediction, yet their effectiveness across application specific chemical targets, especially polymers, remains highly underexplored. In particular, for photo-sensitive polymers commonly used in photolithography, predictive accuracy depends on capturing subtle structure-property relationships involving nonlinear optical response, excited-state energetics, and physicochemical constraints. To this end, we present a systematic benchmark, called Polymer-Arena, of representative multi-task graph learning models for photo-sensitive polymer property prediction, covering classical message-passing, geometry-aware, and transformer-based architectures under a unified evaluation protocol. By controlling the dataset composition, molecular featurization, and hyperparameter tuning, we isolate architectural inductive biases that govern predictive accuracy, generalization, and stability. Our results reveal clear trade-offs between messagepassing depth, geometric encoding, and attention mechanisms, with no single architecture consistently dominating across tasks. Models incorporating directional or continuous-filter information excel on excited-state and nonlinear optical targets, while simpler message-passing architectures remain competitive on scalar physicochemical properties. These findings provide actionable guidance for model selection in datadriven polymer discovery and establish a reproducible baseline for future graph-based molecular learning. Dataset is available at https: //zenodo.org/records/18228299.