Deep Learning for Polymer Informatics: A Critical Analysis of Representations, Architectures, and Evaluation Practices

Abstract

Polymer science stands at a compelling intersection with deep learning, as computational methods increasingly complement experimental discovery by accelerating property prediction, inverse design, and process optimization. This review presents a critical and constructive synthesis of deep learning in polymer informatics, organized around four interconnected pillars: representation, evaluation, physics integration, and multi-modality. We argue that realizing deep learning’s full potential requires not only architectural innovation but also domain-aware representations that encode the statistical ensemble nature of polymers, evaluation protocols aligned with discovery scenarios, and physically grounded inductive biases. Polymers present unique challenges for deep learning methods originally designed for small molecules or natural language. Unlike discrete, well-defined structures, polymers are statistical ensembles of chains with variable lengths, sequences, tacticities, and morphologies. Representations that reduce this complexity to a single deterministic chain, while useful in practice, cannot capture ensemble-level variability that governs bulk performance. Similarly, evaluation practices that ignore the clustered nature of polymer chemical space risk overstating a model’s practical utility for exploring novel chemistries. To address this, we introduce the Validation Gap, the systematic divergence between benchmark performance and prospective experimental utility, as a diagnostic framework to help the community identify where further progress is most needed. We critically analyze architectures ranging from feedforward networks and graph neural networks to Transformers, generative models, and physics-informed networks through the lens of their alignment with polymer physical reality. One hypothesis emerging from this review’s cross-study synthesis is that multi-modal architectures may exhibit a smaller gap between random-split and scaffold-split performance, consistent with the intuition that representational breadth supports generalization across diverse chemistries; this remains an open question requiring controlled validation. To promote transparency and reproducibility, we propose the Polymer Informatics Minimum Reporting Requirements (PIMRR) as a community standard, formalized through a machine-readable metadata schema. We conclude with a tiered research roadmap emphasizing that the most impactful near-term investments lie in data infrastructure, standardized benchmarks, and ensemble-aware representations, foundations that will amplify the value of subsequent architectural advances.