# Polymer Informatics: Current Status and Critical Next Steps

arXiv: https://arxiv.org/abs/2011.00508 · HTML: https://mlsanity.com/paper/2011.00508/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 29 of 29)

- [Graph Rationalization with Environment-based Augmentations](https://mlsanity.com/paper/2206.02886/) (2022)
- [polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics](https://mlsanity.com/paper/2209.14803/) (2022)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing](https://mlsanity.com/paper/2209.13136/) (2022)
- [A graph representation of molecular ensembles for polymer property prediction](https://mlsanity.com/paper/2205.08619/) (2022)
- [MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction](https://mlsanity.com/paper/2406.04727/) (2024)
- [Copolymer Informatics with Multi-Task Deep Neural Networks](https://mlsanity.com/paper/2103.14174/) (2021)
- [Bioplastic Design using Multitask Deep Neural Networks](https://mlsanity.com/paper/2203.12033/) (2022)
- [PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit Augmentations](https://mlsanity.com/paper/2408.07556/) (2024)
- [Transferring a molecular foundation model for polymer property predictions](https://mlsanity.com/paper/2310.16958/) (2023)
- [Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions](https://mlsanity.com/paper/2205.13757/) (2022)
- [PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models](https://mlsanity.com/paper/2504.08859/) (2025)
- [Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation](https://mlsanity.com/paper/2401.02008/) (2024)
- [Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency](https://mlsanity.com/paper/2407.15273/) (2024)
- [Photonic Quantum Computing For Polymer Classification](https://mlsanity.com/paper/2211.12207/) (2022)
- [Joint Embedding Predictive Architecture for self-supervised pretraining on polymer molecular graphs](https://mlsanity.com/paper/2506.18194/) (2025)
- [A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers](https://mlsanity.com/paper/2608.06694/) (2026)
- [GLAMOUR: Graph Learning over Macromolecule Representations](https://mlsanity.com/paper/2103.02565/) (2021)
- [Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction](https://mlsanity.com/paper/2309.01788/) (2023)
- [POINT2: A Polymer Informatics Training and Testing Database](https://mlsanity.com/paper/2503.23491/) (2025)
- [polyGen: A Learning Framework for Atomic-level Polymer Structure Generation](https://mlsanity.com/paper/2504.17656/) (2025)
- [Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films](https://mlsanity.com/paper/2505.23064/) (2025)
- [Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers](https://mlsanity.com/paper/2506.01994/) (2025)
- [A Conformation-Centric Generative Foundation Model for Linear Polymer Modeling and Design](https://mlsanity.com/paper/2510.16023/) (2025)
- [PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning](https://mlsanity.com/paper/2512.13186/) (2025)
- [Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose?](https://mlsanity.com/paper/2605.08255/) (2026)
- [It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction](https://mlsanity.com/paper/2605.10551/) (2026)
- [Periodic Topological Deep Learning for Polymer Design and Discovery](https://mlsanity.com/paper/2605.26833/) (2026)
- [An open benchmark for machine learning-based polymer property prediction](https://mlsanity.com/paper/2609.27036/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.