PolyBC: A Graph-Augmented Chemical Language Model for Complex Copolymer Learning

Copolymers are macromolecules assembled from repeating structural units, arranged in random, alternating, or block architectures. Predicting and designing copolymer properties from molecular structure is of broad interest across energy storage, construction, medicine, and aerospace, yet remains fundamentally challenging because (1) there is no effective parameterization for the structural units or for the complex architectures connecting them, and (2) experimental measurements for any particular application are typically scarce and unbalanced. To resolve these challenges, we propose PolyBC, a physics-guided, graph-augmented chemical language model. In particular, we introduce a graph neural network over structural units at two levels of granularity — repeat units and sub-monomer fragments — whose nodes are encoded by Poly4mer, a chemical language model that supplies transferable latent embeddings of homopolymer segments. At either resolution, the GNN reasons over the polymer topology and closes the finite chain with architecture-matched boundary conditions: periodic for alternating copolymers, Neumann for block copolymers, and mean-field for random copolymers. This construction imposes a theoretically guaranteed, consistent bulk physics: training on one copolymer type and boundary condition generalizes to the others, and predictions are invariant to how many repeats are drawn. On both synthetic and experimental benchmarks, our model is repetition-invariant, data-efficient, and transferable across copolymer types at both granularities, outperforming state-of-the-art GNN and LLM baselines on prediction and design tasks.