We address polymer property prediction with a multi-view design that exploits complementary representations. Our system integrates four families: (i) tabular RDKit/Morgan descriptors, (ii) graph neural networks, (iii) 3D-informed representations, and (iv) pretrained SMILES language models, and averages per-property predictions via a uniform ensemble. Models are trained with 10-fold splits and evaluated with SMILES test-time augmentation. The approach ranks 9th of 2241 teams in the Open Polymer Prediction Challenge at NeurIPS 2025. The submitted ensemble achieves a public MAE of 0.057 and a private MAE of 0.082.
Cite
@article{arxiv.2511.10893,
title = {Multi-View Polymer Representations for the Open Polymer Prediction},
author = {Wonjin Jung and Yongseok Choi},
journal= {arXiv preprint arXiv:2511.10893},
year = {2025}
}
Comments
The authors have decided to withdraw this manuscript due to internal approval and authorship issues. A revised version may be posted in the future