RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction

This repository is an official PyTorch(Geometric) implementation of RingFormer in "RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction".

Requirements

To install requirements:

pip install -r requirements.txt

Datasets

DATASET	#GRAPHS	AVG. # NODES	AVG. # EDGES	AVG. # RINGS
CEPDB	2.2M	27.6	33.3	6.7
HOPV	350	42.7	49.3	7.5
PFD	1055	77.1	84.2	8.2
NFA	654	118.2	133.0	15.8
PD	277	80.7	88.2	8.5

We provide two complementary dataset releases:

• Processed ring-graph data (this GitHub repo):
  This repository includes the processed ring graphs used by RingFormer (e.g., ring-enhanced graph structures/tensors ready for training and evaluation). Please follow the instructions in this repo to generate/load the ring graphs and reproduce the paper results.

• Raw CSV tables (🤗 Hugging Face):
  The original/raw CSV datasets (CEPDB / HOPV / PFD / NFA / PD) are hosted on Hugging Face at:
  https://huggingface.co/datasets/Tommy-DING/organic-solar-cell-molecule-property-prediction
  Use this release if you want to inspect the raw columns, reprocess the data with your own featurization, or build alternative graph constructions.

Training & Evaluation

• To construct ring graphs, run this command:

python generate_ring_graphs.py --dataset <dataset> 

• To train the model(s) in the paper, run this command:

python train.py --dataset <dataset> 

--dataset: ('HOPV', 'PFD', 'NFA', 'PD', 'CEPDB')

Citation

@inproceedings{ding2025ringformer,
  title={RingFormer: a ring-enhanced graph transformer for organic solar cell property prediction},
  author={Ding, Zhihao and Zhang, Ting and Li, Yiran and Shi, Jieming and Zhang, Chen Jason},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={1},
  pages={155--163},
  year={2025}
}