Title: Learning Repetition-Invariant Representations for Polymer Informatics
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Learning

Polymer Informatics

Generalization

Data Augmentation
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the challenge that conventional graph neural networks, which model polymers using only a single repeating unit, produce embeddings that drift as the number of monomer repeats varies. To overcome this, the authors propose Graph Repetition Invariance, which augments polymer graphs with multiple repeat units and aligns GNN message passing to a maximum spanning tree via max‐aggregation and a sparsity penalty—ensuring that only the most informative edges drive representation updates. Three‐unit augmentations suffice for learning truly repeat‐invariant embeddings and demonstrate, across both homopolymer and copolymer benchmarks, that GRIN yields stable, size‐agnostic feature vectors that outperform existing baselines in property prediction tasks\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is generally well written.\n2. The experimental section is comprehensive\n3. It poses an interesting, timely question in the graph-learning domain that could inspire future research. Despite minor issues, the work has the potential to benefit the broader community.\n\nWeakness:\n1. I think there are some points not clearly stated. I will check with them in the question part.\n2. The theoretical part is more akin to intuition than rigorous evidence. \n3. The scale of datasets seems limited\n\nQUESTIONS:\nI don’t quite understand the example from lines 27–33. Could you provide a concrete illustration? Moreover, do large language models exhibit this problem? If not, perhaps we can handle such invariance naturally via augmentations present in real data.\n\nWhat is the meaning of Figure 2? How should we interpret the right-hand panel?\n\nI’d like to confirm the overall pipeline: we start with a set of polymers, extract their monomer units, generate augmented data from those monomers, append it to the training set, and then train a GRIN model. Is that correct?\n\nBeyond these geometric deep learning baselines, what about kernel-based methods? I believe repeating patterns could be addressed using domain knowledge—perhaps via motif-inspired embeddings. Could we design (deep) graph kernels to tackle this problem?\n\nHow would models like Residual Gated Graph ConvNets, which have an inherent sequential design, perform on this task?\n\nThe improvement of the GRIN variant over the baseline—and of training on G₁+G₃ versus G₁ alone—is not very pronounced given the limited training data. Could you explain this? Does it imply that encoding such inductive biases as hard constraints is unnecessary when a large amount of training data is available?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper considers the problem of applying GNNs to polymers, which are composed of repeated structures. They propose a method that (1) encodes polymers as a graph, (2) uses max aggregation GNNs, (3) augments the training data with polymers of different numbers of repeating units, and (4) encourages sparsity with L_1 regularisation. They provide some theoretical results about the correspondence of their model to the MST algorithm, and about how many repeated units need to be added during data augmentation. They perform experiments showing model performance across 6 datasets against a number of baselines, and also perform an ablation study on the repeat size and proportion of augmented graphs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Strong model performance - outperformed the baselines in thorough experiments.\n2. Nice motivation for the solution: invariance to the number of repeating units is a very fitting invariance to encode for the problem being solved.\n3. Generally technically sound - terms were mostly well defined and explained.\n4. Writing was clear, concise, and readable.\n\nWeaknesses:\n1. The claims of the paper are often overstated and do not reflect the actual contribution. This is the primary reason I have assigned a score of 1 for Quality and my biggest reason to reject the paper. My current perspective is that large portions of the paper need to be re-written with the true contributions in mind. To expand on this:\n\n1.1. The paper constantly refers to their embeddings as \"repeat-invariant\". However, nothing about their model guarantees this, and the embeddings shown in Figure 2 show that GRIN is not actually invariant, just approximately so. E.g. on L252, the phrase \"demonstrating true repetition invariance\" is used. By contrast, GCNs are truly invariant to node permutation, and other GNNs are also invariant to operators such as translation and rotation. In the paper, there needs to be a much clearer distinction between true invariance and approximate invariance.\n\n1.2 The paper claims to \"align the model with the MST construction process\" - this is very vague terminology and the actual theory presented in section 3.3.1 does not correspond to the language used. Furthermore, the actual \"alignment\" is between any GNN using max aggregation and a sparsity constraint, so is not particular to the GRIN model. It is also not clear why the \"alignment\" with MST is actually useful to the problem of repetition invariance - this needs to be expanded on.\n\n1.3 The paper phrases their contribution as a new model (GRIN). However, this can be separated into (1) some common choices for GNN architecture (max aggregation and sparsity constraints), (2) a graph encoding scheme, and (3) data augmentation. (2) and (3) seem to be the main novelty of the paper to me, so the messaging should be centred around them, rather than pitching it as a new model.\n\n2. Some of the theory could be made clearer, and the impact of the results more clearly stated up-front. For example, section 3.3.2 was dense and hard to understand - it should be expanded, and the explanation given on L174 of why the theory is useful explained better up-front, so the motivation is clear. At other times, terms are not defined or cited (e.g. \"L-Lipschitz\" on L181).\n\n3. Some motivation is lacking, and could be better shown in the experiments. For example, on L99, it is stated that \"sum grows unbounded\" as motivation for using max aggregation. However, it is not clear why why is this a problem. In practice, the repeat size will not become infinitely large. This argument also applies to any graph as the size increases. This claim could be demonstrated empirically by using sum aggregation vs max in the GRIN framework and showing the benefits of max.\n\n4. Some of the experimental results are underwhelming. For example, the performance gain of GRIN over other models such as BFGIN seems quite marginal. This seems to undercut the thesis of the necessity of repetition invariance.\n\nQUESTIONS:\n1. L51 \"The MST criterion further preserves the most informative backbone of the polymer graph.\" -> what does this mean? It is likewise unclear to me what \"dynamic programming(DP)-style updates enforce a fixed reasoning procedure\" means.\n2. L78 What do you mean by \"causal\" information?\n3. L120 - L121 \"With residual connection h, any node not chosen by max-aggregation simply skips the update and retains its previous embedding unchanged.\" -> What does this mean? And how is it reflected in the above equation?\n4. L149 -> I can see a correspondence between MST and max aggregation, but where here is the \"theoretical guarantee\" that was spoken of?\n5. L257 \"MST-aligned max aggregation\" -> is it not just max aggregation, which itself is \"MST-aligned\"? This phrasing is a bit confusing to me.\n\nMinor comments:\n1. Figure 2 is not explained well in the caption. Also, the figure doesn't show explicitly which polymers belong to the same class (i.e. are variants of one another with a different number of repeating units)\n2. L41 -> could be nice to quantify this, rather than just motivating visually\n3. L104 -> the graph encoding would benefit from a visualisation\n4. L127 \"sparsity constraint\" -> this is the first time the sparsity constraint is mentioned. It is only defined later\n5. L132 -> typo\n\nLIMITATIONS:\nLimitations of the work are not discussed at all. Future work is included in the appendix.\n\nThe authors need to seriously consider their paper and the NeurIPS requirements to discuss limitations, as stated in the checklist item 2.\nThe bullet points under the guidelines provide a thorough explanation of many things the authors could have included in their paper as limitations. As one simple example: the paper should have considered the limitation of using soft invariance vs guaranteed invariance.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GRIN, a novel framework for learning repetition-invariant graph representations of polymers. By combining maximum spanning tree alignment with repeat-unit-based graph augmentation, GRIN enables the model to capture consistent structural patterns across different polymer lengths. Experimental results show that GRIN outperforms strong baselines and generalizes well to unseen long-chain polymers.\n\nSTRENGTHS AND WEAKNESSES:\nstrengths:\n1. The paper addresses a fundamental limitation in existing graph-based polymer modeling: the inability to handle varying repeat unit numbers. This is a practically relevant problem, as real-world polymers vary widely in chain length and structure.\n2. The experiment result looks good to me. It shows consistently strong performance on Test60, even though training only involved 1 or 3 RUs. This validates the method’s core claim of repetition-invariance and scalability.\n\nWeakness:\n1. All baseline models are trained only on 1-RU graphs, while GRIN is trained on both 1-RU and 3-RU graphs. This makes it unclear whether GRIN's performance gain stems from model design or richer input structures.\n2. The model does not enforce repetition-invariance via explicit constraints or loss terms. Instead, it relies on structural heuristics like max-pooling and augmented training data, which may not generalize to more complex repeat structures.\n3. Although GRIN generalizes well to long chains, the paper does not evaluate training or inference cost as the RU number increases. This may raise concerns about efficiency in industrial-scale deployment.\n\nQUESTIONS:\n1. The method is designed for strictly repetitive polymers, but many real-world polymers exhibit branching or block copolymer patterns that deviate from perfect repetition. Have the authors explored how GRIN behaves on polymers with irregular or partially repeating structures?\n\n2. While the paper claims that three RUs are sufficient, it would be helpful to evaluate whether this number is optimal or if performance significantly changes with more or fewer units.\n\nLIMITATIONS:\n1. Baseline models are only trained on single-RU graphs, while GRIN uses both single- and multi-RU graphs, potentially conflating input richness with model capability.\n\n2. The framework does not include an explicit mechanism (e.g., regularization or constraints) to guarantee invariance to repeat unit count, which may limit interpretability and control.\n\n3. Although GRIN performs well on 60-RU graphs, the paper does not provide runtime or memory benchmarks, which are critical for real-world deployment.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
The authors introduce a method to learn polymer representations that are invariant to the number of repeating units in their graph representations. The proposed method outperforms state-of-the-art baselines on both homopolymer and copolymer benchmarks.

STRENGTHS AND WEAKNESSES:
##Strengths
- The problem is well-motivated and is important problem.
- The paper is clear and the method is described well.

## Weaknesses
- Although authors compare their method to variants of GIN and GCN, but I would like to see how this behaves with Graph Transformers.
- Experiments can be extended to larger datasets.

QUESTIONS:
see weaknesses

LIMITATIONS:
NA
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-05