Learning Repetition-Invariant Representations for Polymer Informatics
This addresses a domain-specific challenge in polymer informatics for materials science applications, offering an incremental improvement over existing methods.
The paper tackled the problem of inconsistent vector representations for polymers with varying numbers of repeating units in graph neural networks, introducing Graph Repetition Invariance (GRIN) which outperformed state-of-the-art baselines on homopolymer and copolymer benchmarks.
Polymers are large macromolecules composed of repeating structural units known as monomers and are widely applied in fields such as energy storage, construction, medicine, and aerospace. However, existing graph neural network methods, though effective for small molecules, only model the single unit of polymers and fail to produce consistent vector representations for the true polymer structure with varying numbers of units. To address this challenge, we introduce Graph Repetition Invariance (GRIN), a novel method to learn polymer representations that are invariant to the number of repeating units in their graph representations. GRIN integrates a graph-based maximum spanning tree alignment with repeat-unit augmentation to ensure structural consistency. We provide theoretical guarantees for repetition-invariance from both model and data perspectives, demonstrating that three repeating units are the minimal augmentation required for optimal invariant representation learning. GRIN outperforms state-of-the-art baselines on both homopolymer and copolymer benchmarks, learning stable, repetition-invariant representations that generalize effectively to polymer chains of unseen sizes.