Learning Repetition-Invariant Representations for Polymer Informatics

TL;DR

This paper introduces GRIN, a novel graph neural network method that learns polymer representations invariant to the number of repeating units, enabling better generalization across polymer sizes.

Contribution

The paper proposes the GRIN method, combining graph alignment and augmentation, with theoretical guarantees for learning repetition-invariant polymer representations.

Findings

GRIN outperforms existing methods on polymer benchmarks.

It provides stable, size-invariant representations for polymers.

Theoretical analysis shows three units as minimal augmentation for invariance.

Abstract

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.