# A graph representation of molecular ensembles for polymer property prediction

arXiv: https://arxiv.org/abs/2205.08619 · HTML: https://mlsanity.com/paper/2205.08619/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 115 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 16 of 16)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Molecular Graph Convolutions: Moving Beyond Fingerprints](https://mlsanity.com/paper/1603.00856/) (2016)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [Molecular representation learning with language models and domain-relevant auxiliary tasks](https://mlsanity.com/paper/2011.13230/) (2020)
- [Message-passing neural networks for high-throughput polymer screening](https://mlsanity.com/paper/1807.10363/) (2018)
- [Polymer Informatics with Multi-Task Learning](https://mlsanity.com/paper/2010.15166/) (2020)
- [Polymer Informatics: Current Status and Critical Next Steps](https://mlsanity.com/paper/2011.00508/) (2020)
- [Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules](https://mlsanity.com/paper/1910.10685/) (2019)
- [Neural Message Passing on High Order Paths](https://mlsanity.com/paper/2002.10413/) (2020)
- [Machine learning on DNA-encoded libraries: A new paradigm for hit-finding](https://mlsanity.com/paper/2002.02530/) (2020)
- [Copolymer Informatics with Multi-Task Deep Neural Networks](https://mlsanity.com/paper/2103.14174/) (2021)

## Built on by (most cited first, 20 of 20)

- [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)
- [Representing Molecules as Random Walks Over Interpretable Grammars](https://mlsanity.com/paper/2403.08147/) (2024)
- [Inverse Design of Copolymers Including Stoichiometry and Chain Architecture](https://mlsanity.com/paper/2410.02824/) (2024)
- [Molecular Machine Learning in Chemical Process Design](https://mlsanity.com/paper/2508.20527/) (2025)
- [Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design](https://mlsanity.com/paper/2408.11793/) (2024)
- [Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials](https://mlsanity.com/paper/2404.10746/) (2024)
- [Molecular topological deep learning for polymer property prediction](https://mlsanity.com/paper/2410.04765/) (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)
- [HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design](https://mlsanity.com/paper/2609.02746/) (2026)
- [Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios](https://mlsanity.com/paper/2301.01231/) (2023)
- [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)
- [Learning Repetition-Invariant Representations for Polymer Informatics](https://mlsanity.com/paper/2505.10726/) (2025)
- [Learning ORDER-Aware Multimodal Representations for Composite Materials Design](https://mlsanity.com/paper/2602.02513/) (2026)
- [PolyMon: A Unified Framework for Polymer Property Prediction](https://mlsanity.com/paper/2603.13303/) (2026)
- [Periodic Topological Deep Learning for Polymer Design and Discovery](https://mlsanity.com/paper/2605.26833/) (2026)
- [Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation](https://mlsanity.com/paper/2609.16028/) (2026)
- [An open benchmark for machine learning-based polymer property prediction](https://mlsanity.com/paper/2609.27036/) (2026)

## Abstract (arXiv)

Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties. However, in contrast to organic molecules, polymers are often not well-defined single structures but an ensemble of similar molecules, which poses unique challenges to traditional chemical representations and machine learning approaches. Here, we introduce a graph representation of molecular ensembles and an associated graph neural network architecture that is tailored to polymer property prediction. We demonstrate that this approach captures critical features of polymeric materials, like chain architecture, monomer stoichiometry, and degree of polymerization, and achieves superior accuracy to off-the-shelf cheminformatics methodologies. While doing so, we built a dataset of simulated electron affinity and ionization potential values for >40k polymers with varying monomer composition, stoichiometry, and chain architecture, which may be used in the development of other tailored machine learning approaches. The dataset and machine learning models presented in this work pave the path toward new classes of algorithms for polymer informatics and, more broadly, introduce a framework for the modeling of molecular ensembles.