Polygrammar: Grammar for Digital Polymer Representation and Generation
Name
Advanced Science - 2022 - Guo - Polygrammar Grammar for Digital Polymer Representation and Generation.pdf
Description
Published version
Size
4.55 MB
Format
Adobe PDF
Checksum (MD5)
6501298fc7cf66253452e85711209af7
Author(s)
Guo, Minghao
Shou, Wan
Makatura, Liane
Erps, Timothy
Foshey, Michael
Matusik, Wojciech
Date Issued
June 9, 2022
Journal
Advanced Science
Publisher
Wiley
Citation
Guo, Minghao, Shou, Wan, Makatura, Liane, Erps, Timothy, Foshey, Michael et al. 2022. "Polygrammar: Grammar for Digital Polymer Representation and Generation." Advanced Science.
Version
Final published version
Abstract
Polymers are widely studied materials with diverse properties and applications determined by molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches cannot offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, a parametric, context-sensitive grammar designed specifically for polymers (PolyGrammar) is proposed. Using the symbolic hypergraph representation and 14 simple production rules, PolyGrammar can represent and generate all valid polyurethane structures. An algorithm is presented to translate any polyurethane structure from the popular Simplified Molecular-Input Line-entry System (SMILES) string format into the PolyGrammar representation. The representative power of PolyGrammar is tested by translating a dataset of over 600 polyurethane samples collected from the literature. Furthermore, it is shown that PolyGrammar can be easily extended to other copolymers and homopolymers. By offering a complete, explicit representation scheme and an explainable generative model with validity guarantees, PolyGrammar takes an essential 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/advs.202101864
