mlsanityData to 14 Oct 2026

Polygrammar: Grammar for Digital Polymer Representation and Generation

arXiv 2105.05278 ↗First posted 5 May 2021physics.chem-ph cond-mat.mtrl-sci cs.LGcited by 8

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Abstract (arXiv)

Polymers are widely-studied materials with diverse properties and applications determined by different molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches are unable to offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, we present a parametric, context-sensitive grammar designed specifically for the representation and generation of polymers. As a demonstrative example, we implement our grammar for polyurethanes. Using our symbolic hypergraph representation and 14 simple production rules, our PolyGrammar is able to represent and generate all valid polyurethane structures. We also present an algorithm to translate any polyurethane structure from the popular SMILES string format into our PolyGrammar representation. We test the representative power of PolyGrammar by translating a dataset of over 600 polyurethane samples collected from literature. Furthermore, we show that PolyGrammar can be easily extended to the other copolymers and homopolymers such as polyacrylates. By offering a complete, explicit representation scheme and an explainable generative model with validity guarantees, our PolyGrammar takes an important step toward a more comprehensive and practical system for polymer discovery and exploration. As the first bridge between formal languages and chemistry, PolyGrammar also serves as a critical blueprint to inform the design of similar grammars for other chemistries, including organic and inorganic molecules.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 17 of its 86 references that are in the map. First posted on arXiv 5 May 2021.

Builds on

17 of 86 references.

References · 17

  1. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  2. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 652
  3. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 364
  4. 2017Molecular De Novo Design through Deep Reinforcement Learningcited by 267
  5. 2018Learning Deep Generative Models of Graphscited by 229
  6. 2020Hierarchical Generation of Molecular Graphs using Structural Motifscited by 208
  7. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 200
  8. 2017Learning to Plan Chemical Synthesescited by 179
  9. 2018Syntax-Directed Variational Autoencoder for Structured Datacited by 103
  10. 2017Retrosynthetic reaction prediction using neural sequence-to-sequence modelscited by 86
  11. 2018Molecular generative model based on conditional variational autoencoder for de novo molecular designcited by 73
  12. 2019Mol-CycleGAN - a generative model for molecular optimizationcited by 66
5 more
  1. 2018Molecular Hypergraph Grammar with its Application to Molecular Optimizationcited by 56
  2. 2017Molecular Generation with Recurrent Neural Networks (RNNs)cited by 36
  3. 2017Learning Heuristic Search via Imitationcited by 34
  4. 2017Application of generative autoencoder in de novo molecular designcited by 32
  5. 2018CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networkscited by 31

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 1; 2026: 1.

8 papers.

  1. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  2. 2024Representing Molecules as Random Walks Over Interpretable Grammarscited by 7
  3. 2024Inverse Design of Copolymers Including Stoichiometry and Chain Architecturecited by 7
  4. 2023DISeR: Designing Imaging Systems with Reinforcement Learningcited by 6
  5. 2024GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNscited by 3
  6. 2026From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistrycited by 1
  7. 2023Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Predictioncited by 0
  8. 2025Exploring polymer classification with a hybrid single-photon quantum approachcited by 0