Create README.md

README.md

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+---
+pretty_name: RingFormer Raw CSV (HOPV / PFD / NFA / PD)
+license: other
+task_categories:
+- tabular-regression
+tags:
+- chemistry
+- organic-solar-cells
+- molecular-property-prediction
+- photovoltaics
+- graph-machine-learning
+- ringformer
+language:
+- en
+---
+
+# RingFormer Raw CSV Dataset
+
+## Summary
+This Hugging Face dataset repository publishes the **original/raw CSV tables** used in the RingFormer project, including four datasets: **HOPV, PFD, NFA, and PD**.
+
+This data is used in the paper *“RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction”* (AAAI 2025) for organic solar cell (OSC) property/device-metric prediction.
+
+| 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          |
+
+**Important:**
+- This repository contains *raw/tabular CSV files only*.
+- The **processed ring-graph data** (and the code to generate it) is available in the RingFormer GitHub repository, not here. Replace the placeholder below with your actual link:
+  - GitHub: `https://github.com/TommyDzh/RingFormer`
+
+
+
+## Schema (Columns)
+
+### `hopv.csv`
+Header:
+`smiles,doi,inchlKEY,construction,architecture,complement,HOMO,LUMO,electrochemical_gap,optical_gap,PCE,V_OC,J_SC,fill_factor`
+
+- `smiles`: molecule SMILES
+- `doi`: reference DOI
+- `inchlKEY`: InChIKey (kept as-is from the original file)
+- `construction`: material category (e.g., polymer)
+- `architecture`: device architecture (e.g., bulk)
+- `complement`: complementary material (e.g., PC61BM/PC71BM)
+- `HOMO`, `LUMO`: energy levels (commonly in eV; values appear as negatives in the file)
+- `electrochemical_gap`, `optical_gap`: gap-related quantities (commonly in eV)
+- `PCE`: power conversion efficiency
+- `V_OC`: open-circuit voltage
+- `J_SC`: short-circuit current density
+- `fill_factor`: fill factor (raw data may store it as percent-like values)
+
+Missing values can appear as `nan`.
+
+### `PFD.csv`
+Header:
+`Nickname,PCE_max(%),PCE_ave(%),Voc,Jsc,FF,Mw,Mn,PDI,Monomer,HOMO,LUMO,bandgap,SMILES`
+
+- `Nickname`: material/polymer nickname
+- `PCE_max(%)`, `PCE_ave(%)`: max/average PCE (percent)
+- `Voc`, `Jsc`, `FF`: device metrics
+- `Mw`, `Mn`, `PDI`: molecular weight related fields
+- `Monomer`: monomer-related field (kept as-is)
+- `HOMO`, `LUMO`, `bandgap`: electronic properties
+- `SMILES`: structure SMILES
+
+### `NFA.csv` and `PD.csv`
+Both share the same header (kept exactly as in the original CSV, including quotes/hyphens):
+`PCE_max(%),PCE_ave(%),Jsc(mA/cm2),FF,Voc(V),HOMO_n(eV),'-LUMO_n(eV),Eg_n(eV),n(SMILES),M (g/mol),HOMO_n(eV),'-LUMO_n(eV),Eg_n(eV),p(SMILES),Mw (kg/mol),Mn(kg/mol),PDI`
+
+This can be interpreted as a paired-material record (n / p) with device performance:
+- `PCE_max(%)`, `PCE_ave(%)`, `Jsc(mA/cm2)`, `FF`, `Voc(V)`: device performance
+- First group (`... n(SMILES)`): n-side material properties and SMILES
+- `M (g/mol)`: molecular mass field (unit in header)
+- Second group (`... p(SMILES)`): p-side material properties and SMILES (column names are not renamed in the raw file)
+- `Mw (kg/mol)`, `Mn(kg/mol)`, `PDI`: molecular-weight related fields (units in headers)
+
+## How to Use
+This repository provides raw CSVs. Example:
+
+```python
+import pandas as pd
+
+df_hopv = pd.read_csv("hopv.csv")
+df_pfd  = pd.read_csv("PFD.csv")
+df_nfa  = pd.read_csv("NFA.csv")
+df_pd   = pd.read_csv("PD.csv")
+```
+
+For **ring-graph construction** and **training**, use the scripts in the RingFormer GitHub repository (e.g., `generate_ring_graphs.py`, `train.py` as described in the upstream README).
+
+## Citation
+If you use this dataset, please cite:
+
+```bibtex
+@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}
+}
+```
+
+## License
+This repository republishes the original CSV tables used by RingFormer for reproducibility and re-processing. Please refer to the upstream RingFormer GitHub repository for licensing/usage terms of the data. If the upstream repository does not clearly specify a license for the data, please verify compliance before downstream use.