# Graph Convolutional Neural Networks for Polymers Property Prediction

arXiv: https://arxiv.org/abs/1811.06231 · HTML: https://mlsanity.com/paper/1811.06231/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 9 of 9)

- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit Augmentations](https://mlsanity.com/paper/2408.07556/) (2024)
- [Multimodal machine learning with large language embedding model for polymer property prediction](https://mlsanity.com/paper/2503.22962/) (2025)
- [Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties](https://mlsanity.com/paper/2101.05339/) (2021)
- [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)
- [PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning](https://mlsanity.com/paper/2512.13186/) (2025)
- [Exploring polymer classification with a hybrid single-photon quantum approach](https://mlsanity.com/paper/2512.18125/) (2025)
- [Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers](https://mlsanity.com/paper/2603.16925/) (2026)

## Abstract (arXiv)

A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design. In this work, we apply graph convolutional neural networks (GCNN) to predict the dielectric constant and energy bandgap of polymers. Using density functional theory (DFT) calculated properties as the ground truth, GCNN can achieve remarkable agreement with DFT results. Moreover, we show that GCNN outperforms other machine learning algorithms. Our work proves that GCNN relies only on morphological data of polymers and removes the requirement for complicated hand-crafted descriptors, while still offering accuracy in fast predictions.